Signal processing apparatus

ABSTRACT

A signal processor which acquires a first signal, including a first primary signal portion and a first secondary signal portion, and a second signal, including a second primary signal portion and a second secondary signal portion, wherein the first and second primary signal portions are correlated. The signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signals may be acquired by measuring energy generated by the medium. A processor of the present invention generates a primary or secondary reference signal which is a combination, respectively, of only the primary or secondary signal portions. The secondary reference signal is then used to remove the secondary portion of each of the first and second measured signals via a correlation canceler, such as an adaptive noise canceler, preferably of the joint process estimator type. The primary reference signal is used to remove the primary portion of each of the first and second measured signals via a correlation canceler. The processor of the present invention may be employed in conjunction with a correlation canceler in physiological monitors wherein the known properties of energy attenuation through a medium are used to determine physiological characteristics of the medium. Many physiological conditions, such as the pulse, or blood pressure of a patient or the concentration of a constituent in a medium, can be determined from the primary or secondary portions of the signal after other signal portion is removed.

This application is a continuation-in-part of U.S. Ser. No. 08/249,690,filed May 26, 1994, now allowed, which is a continuation of applicationSer. No. 07/666,060, filed Mar. 7, 1991, now abandoned.

FIELD OF THE INVENTION

The present invention relates to the field of signal processing. Morespecifically, the present invention relates to the processing ofmeasured signals, containing a primary and a secondary signal, for theremoval or derivation of either the primary or secondary signal whenlittle is known about either of these components. The present inventionalso relates to the use of a novel processor which in conjunction with acorrelation canceler, such as an adaptive noise canceler, producesprimary and/or secondary signals. The present invention is especiallyuseful for physiological monitoring systems including blood oxygensaturation.

BACKGROUND OF THE INVENTION

Signal processors are typically employed to remove or derive either theprimary or secondary signal portion from a composite measured signalincluding a primary signal portion and a secondary signal portion. Ifthe secondary signal portion occupies a different frequency spectrumthan the primary signal portion, then conventional filtering techniquessuch as low pass, band pass, and high pass filtering could be used toremove or derive either the primary or the secondary signal portion fromthe total signal. Fixed single or multiple notch filters could also beemployed if the primary and/or secondary signal portion(s) exit at afixed frequency(s).

It is often the case that an overlap in frequency spectrum between theprimary and secondary signal portions exists. Complicating mattersfurther, the statistical properties of one or both of the primary andsecondary signal portions change with time. In such cases, conventionalfiltering techniques are totally ineffective in extracting either theprimary or secondary signal. If, however, a description of either theprimary or secondary signal portion can be made available correlationcanceling, such as adaptive noise canceling, can be employed to removeeither the primary or secondary signal portion of the signal leaving theother portion available for measurement.

Correlation cancelers, such as adaptive noise cancelers, dynamicallychange their transfer function to adapt to and remove either the primaryor secondary signal portions of a composite signal. Correlationcancelers require either a secondary reference or a primary referencewhich is correlated to either the secondary signal or the primary signalportions only. The reference signals are not necessarily arepresentation of the primary or secondary signal portions, but have afrequency spectrum which is similar to that of the primary or secondarysignal portions. In many cases, it requires considerable ingenuity todetermine a reference signal since nothing is usually known a prioriabout the secondary and/or primary signal portions.

One area where composite measured signals comprising a primary signalportion and a secondary signal portion about which no information caneasily be determined is physiological monitoring. Physiologicalmonitoring apparatuses generally measure signals derived from aphysiological system, such as the human body. Measurements which aretypically taken with physiological monitoring systems includeelectrocardiographs, blood pressure, blood gas saturation (such asoxygen saturation), capnographs, heart rate, respiration rate, and depthof anesthesia, for example. Other types of measurements include thosewhich measure the pressure and quantity of a substance within the bodysuch as breathalyzer testing, drug testing, cholesterol testing, glucosetesting, arterial carbon dioxide testing, protein testing, and carbonmonoxide testing, for example. Complications arising in thesemeasurements are often due to motion of the patient, both external andinternal (muscle movement, for example), during the measurement process.

Knowledge of physiological systems, such as the amount of oxygen in apatient's blood, can be critical, for example during surgery. These datacan be determined by a lengthy invasive procedure of extracting andtesting matter, such as blood, from a patient, or by more expedient,non-invasive measures. Many types of non-invasive measurements can bemade by using the known properties of energy attenuation as a selectedform of energy passes through a medium.

Energy is caused to be incident on a medium either derived from orcontained within a patient and the amplitude of transmitted or reflectedenergy is then measured. The amount of attenuation of the incidentenergy caused by the medium is strongly dependent on the thickness andcomposition of the medium through which the energy must pass as well asthe specific form of energy selected. Information about a physiologicalsystem can be derived from data taken from the attenuated signal of theincident energy transmitted through the medium if either the primary orsecondary signal of the composite measurement signal can be removed.However, non-invasive measurements often do not afford the opportunityto selectively observe the interference causing either the primary orsecondary signal portions, making it difficult to extract either one ofthem from the composite signal.

The primary and/or secondary signal portions often originate from bothAC and/or DC sources. The DC portions are caused by transmission of theenergy through differing media which are of relatively constantthickness within the body, such as bone, tissue, skin, blood, etc. Theseportions are easy to remove from a composite signal. The AC componentsare caused by physiological pulsations or when differing media beingmeasured are perturbed and thus, change in thickness while themeasurement is being made. Since most materials in and derived from thebody are easily compressed, the thickness of such matter changes if thepatient moves during a non-invasive physiological measurement. Patientmovement, muscular movement and vessel movement, can cause theproperties of energy attenuation to vary erratically. Traditional signalfiltering techniques are frequently totally ineffective and grosslydeficient in removing these motion induced effects from a signal. Theerratic or unpredictable nature of motion induced signal components isthe major obstacle in removing or deriving them. Thus, presentlyavailable physiological monitors generally become totally inoperativeduring time periods when the measurement site is perturbed.

A blood gas monitor is one example of a physiological monitoring systemwhich is based upon the measurement of energy attenuated by biologicaltissues or substances. Blood gas monitors transmit light into the tissueand measure the attenuation of the light as a function of time. Theoutput signal of a blood gas monitor which is sensitive to the arterialblood flow contains a component which is a waveform representative ofthe patient's arterial pulse. This type of signal, which contains acomponent related to the patient's pulse, is called a plethysmographicwave, and is shown in FIG. 1 as curve s. Plethysmographic waveforms areused in blood pressure or blood gas saturation measurements, forexample. As the heart beats, the amount of blood in the arteriesincreases and decreases, causing increases and decreases in energyattenuation, illustrated by the cyclic wave s in FIG. 1.

Typically, a digit such as a finger, an ear lobe, or other portion ofthe body where blood flows close to the skin, is employed as the mediumthrough which light energy is transmitted for blood gas attenuationmeasurements. The finger comprises skin, fat, bone, muscle, etc., shownschematically in FIG. 2, each of which attenuates energy incident on thefinger in a generally predictable and constant manner. However, whenfleshy portions of the finger are compressed erratically, for example bymotion of the finger, energy attenuation becomes erratic.

An example of a more realistic measured waveform S is shown in FIG. 3,illustrating the effect of motion. The primary plethysmographic waveformportion of the signal s is the waveform representative of the pulse,corresponding to the sawtooth-like pattern wave in FIG. 1. The large,secondary motion-induced excursions in signal amplitude hide the primaryplethysmographic signal s. It is easy to see how even small variationsin amplitude make it difficult to distinguish the primary signal s inthe presence of a secondary signal component n.

A specific example of a blood gas monitoring apparatus is a pulseoximeter which measures the arterial saturation of oxygen in the blood.The pumping of the heart forces freshly oxygenated blood into thearteries causing greater energy attenuation. The arterial saturation ofoxygenated blood may be determined from the depth of the valleysrelative to the peaks of two plethysmographic waveforms measured atseparate wavelengths. Patient movement introduces signal portions mostlydue to venous blood, or motion artifacts, to the plethysmographicwaveform illustrated in FIG. 3. It is these motion artifacts which mustbe removed from the measured signal for the oximeter to continue themeasurement of arterial blood oxygen saturation, even during periodswhen the patient moves. It is also these motion artifacts which must bederived from the measured signal for the oximeter to obtain an estimateof venous blood oxygen saturation. Once the signal components due toeither arterial blood or venous blood is known, its corresponding oxygensaturation may be determined.

SUMMARY OF THE INVENTION

This invention is an improvement of U.S. patent application Ser. No.07/666,060 filed Mar. 7, 1991 and entitled Signal Processing Apparatusand Method, which earlier application has been assigned to the assigneeof the instant application. The invention is a signal processor whichacquires a first signal and a second signal that is correlated to thefirst signal. The first signal comprises a first primary signal portionand a first secondary signal portion. The second signal comprises asecond primary signal portion and a second secondary signal portion. Thesignals may be acquired by propagating energy through a medium andmeasuring an attenuated signal after transmission or reflection.Alternatively, the signals may be acquired by measuring energy generatedby the medium.

The first and second measured signals are processed to generate asecondary reference which does not contain the primary signal portionsfrom either of the first or second measured signals. The remainingsecondary signal portions from the first and second measured signals arecombined to form the secondary reference. This secondary reference iscorrelated to the secondary signal portion of each of the first andsecond measured signals.

The secondary reference is then used to remove the secondary portion ofeach of the first and second measured signals via a correlationcanceler, such as an adaptive noise canceler. The correlation canceleris a device which takes a first and second input and removes from thefirst input all signal components which are correlated to the secondinput. Any unit which performs or nearly performs this function isherein considered to be a correlation canceler. An adaptive correlationcanceler can be described by analogy to a dynamic multiple notch filterwhich dynamically changes its transfer function in response to areference signal and the measured signals to remove frequencies from themeasured signals that are also present in the reference signal. Thus, atypical adaptive correlation canceler receives the signal from which itis desired to remove a component and a reference signal. The output ofthe correlation canceler is a good approximation to the desired signalwith the undesired component removed.

Alternatively, the first and second measured signals may be processed togenerate a primary reference which does not contain the secondary signalportions from either of the first or second measured signals. Theremaining primary signal portions from the first and second measuredsignals are combined to form the primary reference. The primaryreference may then be used to remove the primary portion of each of thefirst and second measured signals via a correlation canceler. The outputof the correlation canceler is a good approximation to the secondarysignal with the primary signal removed and may be used for subsequentprocessing in the same instrument or an auxiliary instrument. In thiscapacity, the approximation to the secondary signal may be used as areference signal for input to a second correlation canceler togetherwith either the first or second measured signals for computation of,respectively, either the first or second primary signal portions.

Physiological monitors can often advantageously employ signal processorsof the present invention. Often in physiological measurements a firstsignal comprising a first primary portion and a first secondary portionand a second signal comprising a second primary portion and a secondsecondary portion are acquired. The signals may be acquired bypropagating energy through a patient's body (or a material which isderived from the body, such as breath, blood, or tissue, for example) orinside a vessel and measuring an attenuated signal after transmission orreflection. Alternatively, the signal may be acquired by measuringenergy generated by a patient's body, such as in electrocardiography.The signals are processed via the signal processor of the presentinvention to acquire either a secondary reference or a primary referencewhich is input to a correlation canceler, such as an adaptive noisecanceler.

One physiological monitoring apparatus which can advantageouslyincorporate the features of the present invention is a monitoring systemwhich determines a signal which is representative of the arterial pulse,called a plethysmographic wave. This signal can be used in bloodpressure calculations, blood gas saturation measurements, etc. Aspecific example of such a use is in pulse oximetry which determines thesaturation of oxygen in the blood. In this configuration, we define theprimary portion of the signal to be the arterial blood contribution toattenuation of energy as it passes through a portion of the body whereblood flows close to the skin. The pumping of the heart causes bloodflow to increase and decrease in the arteries in a periodic fashion,causing periodic attenuation wherein the periodic waveform is theplethysmographic waveform representative of the arterial pulse. Wedefine the secondary portion of the signal to be that which is usuallyconsidered to be noise. This portion of the signal is related to thevenous blood contribution to attenuation of energy as it passes throughthe body. Patient movement causes this component to flow in anunpredictable manner, causing unpredictable attenuation and corruptingthe otherwise periodic plethysmographic waveform. Respiration alsocauses secondary or noise component to vary, although typically at amuch lower frequency than the patients pulse rate.

A physiological monitor particularly adapted to pulse oximetry oxygensaturation measurement comprises two light emitting diodes (LED's) whichemit light at different wavelengths to produce first and second signals.A detector registers the attenuation of the two different energy signalsafter each passes through an absorptive media, for example a digit suchas a finger, or an earlobe. The attenuated signals generally compriseboth primary and secondary signal portions. A static filtering system,such as a bandpass filter, removes a portion of the secondary signalwhich is outside of a known bandwidth of interest, leaving an erratic orrandom secondary signal portion, often caused by motion and oftendifficult to remove, along with the primary signal portion.

Next, a processor of the present invention removes the primary signalportions from the measured signals yielding a secondary reference whichis a combination of the remaining secondary signal portions. Thesecondary reference is correlated to both of the secondary signalportions. The secondary reference and at least one of the measuredsignals are input to a correlation canceler, such as an adaptive noisecanceler, which removes the random or erratic portion of the secondarysignal. This yields a good approximation to the primary plethysmographicsignal as measured at one of the measured signal wavelengths. As isknown in the art, quantitative measurements of the amount of oxygenatedarterial blood in the body can be determined from the plethysmographicsignal in a variety of ways.

The processor of the present invention may also remove the secondarysignal portions from the measured signals yielding a primary referencewhich is a combination of the remaining primary signal portions. Theprimary reference is correlated to both of the primary signal portions.The primary reference and at least one of the measured signals are inputto a correlation canceler which removes the primary portions of themeasured signals. This yields a good approximation to the secondarysignal at one of the measured signal wavelengths. This signal may beuseful for removing secondary signals from an auxiliary instrument aswell as determining venous blood oxygen saturation.

One aspect of the present invention is a signal processor comprising adetector for receiving a first signal which travels along a firstpropagation path and a second signal which travels along a secondpropagation path wherein a portion of the first and second propagationpaths are located in a propagation medium. The first signal has a firstprimary signal portion and a first secondary signal portion and thesecond signal has a second primary signal portion and a second secondarysignal portion. The first and second secondary signal portions are aresult of a change of the propagation medium. This aspect of theinvention additionally comprises a reference processor having an inputfor receiving the first and second signals. The processor is adapted tocombine the first and second signals to generate a secondary referencehaving a significant component which is a function of the first and saidsecond secondary signal portions. The processor may also be adapted tocombine the first and second signals to generate a primary referencehaving a significant component which is a function of the first andsecond primary signal portions

The above described aspect of the present invention may further comprisea signal processor for receiving the secondary reference signal and thefirst signal and for deriving therefrom an output signal having asignificant component which is a function of the first primary signalportion of the first signal. Alternatively, the above described aspectof the present invention may further comprise a signal processor forreceiving the secondary reference signal and the second signal and forderiving therefrom an output signal having a significant component whichis a function of the second primary signal portion of the second signal.Alternatively, the above described aspect of the present invention mayfurther comprise a signal processor for receiving the primary referenceand the first signal and for deriving therefrom an output signal havinga significant component which is a function of the first secondarysignal portion of the signal of the first signal. Alternatively, theabove described aspect of the present invention may further comprise asignal processor for receiving the primary reference and the secondsignal and for deriving therefrom an output signal having a significantcomponent which is a function of the second secondary signal portion ofthe second signal. The signal processor may comprise a correlationcanceler, such as an adaptive noise canceler. The adaptive noisecanceler may comprise a joint process estimator having aleast-squares-lattice predictor and a regression filter.

The detector in the aspect of the signal processor of the presentinvention described above may further comprise a sensor for sensing aphysiological function. The sensor may comprise a light or otherelectromagnetic sensitive device. Additionally, the present inventionmay further comprise a pulse oximeter for measuring oxygen saturation ina living organism. The present invention may further comprise anelectrocardiograph.

Another aspect of the present invention is a physiological monitoringapparatus comprising a detector for receiving a first physiologicalmeasurement signal which travels along a first propagation path and asecond physiological measurement signal which travels along a secondpropagation path. A portion of the first and second propagation pathsbeing located in the same propagation medium. The first signal has afirst primary signal portion and a first secondary signal portion andthe second signal has a second primary signal portion and a secondsecondary signal portion. The physiological monitoring apparatus furthercomprises a reference processor having an input for receiving the firstand second signals. The processor is adapted to combine the first andsecond signals to generate a secondary reference signal having asignificant component which is a function of the first and the secondsecondary signal portions. Alternatively, the processor may be adaptedto combine the first and second signals to generate a primary referencehaving a component which is a function of the first and second primarysignal portions.

The physiological monitoring apparatus may further comprise a signalprocessor for receiving the secondary reference and the first signal andfor deriving therefrom an output signal having a significant componentwhich is a function of the first primary signal portion of the firstsignal. Alternatively, the physiological monitoring apparatus mayfurther comprise a signal processor for receiving the secondaryreference and the second signal and for deriving therefrom an outputsignal having a significant component which is a function of the secondprimary signal portion of the second signal. Alternatively, thephysiological monitoring apparatus may further comprise a signalprocessor for receiving the primary reference and the first signal andderiving therefrom an output signal having a significant component whichis a function of the first secondary signal portion of the first signal.Alternatively, the physiological monitoring apparatus may furthercomprise a signal processor for receiving the primary reference and thesecond signal and deriving therefrom an output signal having asignificant component which is a function of the second secondary signalportion of the second signal.

A further aspect of the present invention is an apparatus for measuringa blood constituent comprising an energy source for directing aplurality of predetermined wavelengths of electromagnetic energy upon aspecimen and a detector for receiving the plurality of predeterminedwavelengths of electromagnetic energy from the specimen. The detectorproduces electrical signals corresponding to the predeterminedwavelengths in response to the electromagnetic energy. At least two ofthe electrical signals are used each having a primary signal portion andan secondary signal portion. Additionally, the apparatus comprises areference processor having an input for receiving the electricalsignals. The processor is configured to combine said electrical signalsto generate a secondary reference having a significant component whichis derived from the secondary signal portions. Alternatively, theprocessor may be configured to combine said signals to generate aprimary reference having a significant component which is derived fromthe primary signal portions.

This aspect of the present invention may further comprise a signalprocessor for receiving the secondary reference and one of the twoelectrical signals and for deriving therefrom an output signal having asignificant component which is a function of the primary signal portionof one of the two electrical signals. Another aspect of the presentinvention may further comprise a signal processor for receiving theprimary reference and one of the two electrical signals and for derivingtherefrom an output signal having a significant component which is afunction of the secondary signal portion of one of the two electricalsignals. This may be accomplished by use of a correlation canceler, suchas an adaptive noise canceler, in the signal processor which may employa joint process estimator having a least-squares-lattice predictor and aregression filter.

Yet another aspect of the present invention is a blood gas monitor fornon-invasively measuring a blood constituent in a body comprising alight source for directing at least two predetermined wavelengths oflight upon a body and a detector for receiving the light from the body.The detector, in response to the light from the body, produces at leasttwo electrical signals corresponding to the at least two predeterminedwavelengths of light. The at least two electrical signals each have aprimary signal portion and a secondary signal portion. The bloodoximeter further comprises a reference processor having an input forreceiving the at least two electrical signals. The processor is adaptedto combine the at least two electrical signals to generate a secondaryreference with a significant component which is derived from thesecondary signal portions. The blood oximeter may further comprise asignal processor for receiving the secondary reference and the twoelectrical signals and for deriving therefrom at least two outputsignals which are substantially equal, respectively, to the primarysignal portions of the electrical signals. Alternatively, the referenceprocessor may be adapted to combine the at least two electrical signalsto generate a primary reference with a significant component which isderived from the primary signal portions. The blood oximeter may furthercomprise a signal processor for receiving the primary reference and thetwo electrical signals and for deriving therefrom at least two outputsignals which are substantially equivalent to the secondary signalportions of the electrical signal. The signal processor may comprise ajoint process estimator.

The present invention also includes a method of determining a secondaryreference from a first signal comprising a first primary signal portionand a first secondary portion and a second signal comprising a secondprimary signal portion and a second secondary portion. The methodcomprises the steps of selecting a signal coefficient which isproportional to a ratio of predetermined attributes of the first primarysignal portion and predetermined attributes of the second primary signalportion. The first signal and the signal coefficient are input into asignal multiplier wherein the first signal is multiplied by the signalcoefficient thereby generating a first intermediate signal. The secondsignal and the first intermediate signal are input into a signalsubtractor wherein the first intermediate signal is subtracted from thesecond signal. This generates a secondary reference having a significantcomponent which is derived from the first and second secondary signalportions.

The present invention also includes a method of determining a primaryreference from a first signal comprising a first primary signal portionand a first secondary signal portion and a second signal comprising asecond primary signal portion and a second secondary signal portion. Themethod comprises the steps of selecting a signal coefficient which isproportional to a ratio of the predetermined attributes of the firstsecondary signal portion and predetermined attributes of the secondsecondary signal portion. The first signal and the signal coefficientare input into a signal multiplier wherein the first signal ismultiplied by the signal coefficient thereby generating a firstintermediate signal. The second signal and the first intermediate signalare input into a signal subtractor wherein the first intermediate signalis subtracted from the second signal. This generates a primary referencehaving a significant component which is derived from the first andsecond primary signal portions. The first and second signals in thismethod may be derived from electromagnetic energy transmitted through anabsorbing medium.

The present invention further embodies a physiological monitoringapparatus comprising means for acquiring a first signal comprising afirst primary signal portion and a first secondary signal portion and asecond signal comprising a second primary signal portion and a secondsecondary signal portion. The physiological monitoring apparatus of thepresent invention also comprises means for determining from the firstand second signals a secondary reference. Additionally, the monitoringapparatus comprises a correlation canceler, such as an adaptive noisecanceler, having a secondary reference input for receiving the secondaryreference and a signal input for receiving the first signal wherein thecorrelation canceler, in real or near real time, generates an outputsignal which approximates the first primary signal portion.Alternatively, the physiological monitoring device may also comprisemeans for determining from the first and second signals a primaryreference. Additionally, the monitoring apparatus comprises acorrelation canceler having a primary reference input for receiving theprimary reference and a signal input for receiving the first signalwherein the correlation canceler, in real or near real time, generatesan output signal which approximates the first secondary signal portion.The correlation canceler may further comprise a joint process estimator.

A further aspect of the present invention is an apparatus for processingan amplitude modulated signal having a signal amplitude complicatingfeature, the apparatus comprising an energy source for directingelectromagnetic energy upon a specimen. Additionally, the apparatuscomprises a detector for acquiring a first amplitude modulated signaland a second amplitude modulated signal. Each of the first and secondsignals has a component containing information about the attenuation ofelectromagnetic energy by the specimen and a signal amplitudecomplicating feature. The apparatus includes a reference processor forreceiving the first and second amplitude modulated signals and derivingtherefrom a secondary reference which is correlated with the signalamplitude complicating feature. Further, the apparatus incorporates acorrelation canceler having a signal input for receiving the firstamplitude modulated signal, a secondary reference input for receivingthe secondary reference, wherein the correlation canceler produces anoutput signal having a significant component which is derived from thecomponent containing information about the attenuation ofelectromagnetic energy by the specimen. Alternatively, the apparatus mayalso include a reference processor for receiving the first and secondamplitude modulated signals and deriving therefrom a primary referencewhich is correlated with the component containing information about theattenuation of electromagnetic energy by the specimen. Further, theapparatus incorporates a correlation canceler having a signal input forreceiving the first amplitude modulated signal, a primary referenceinput for receiving the primary reference, wherein the correlationcanceler produces an output signal having a primary component which isderived from the signal amplitude complicating feature.

Still another aspect of the present invention is an apparatus forextracting a plethysmographic waveform from an amplitude modulatedsignal having a signal amplitude complicating feature, the apparatuscomprising a light source for transmitting light into an organism and adetector for monitoring light from the organism. The detector produces afirst light attenuation signal and a second light attenuation signal,wherein each of the first and second light attenuation signals has acomponent which is representative of a plethysmographic waveform and acomponent which is representative of the signal amplitude complicatingfeature. The apparatus also includes a reference processor for receivingthe first and second light attenuation signals and deriving therefrom asecondary reference. The secondary reference and the signal amplitudecomplicating feature each have a frequency spectrum. The frequencyspectrum of the secondary reference is correlated with the frequencyspectrum of the signal amplitude complicating feature. Additionallyincorporated into this embodiment of the present invention is acorrelation canceler having a signal input for receiving the firstattenuation signal and a secondary reference input for receiving thesecondary reference. The correlation canceler produces an output signalhaving a significant component which is derived from the component whichis representative of a plethysmographic waveform. The apparatus may alsoinclude a reference processor for receiving the first and second lightattenuation signals and deriving therefrom a primary reference.Additionally incorporated in this embodiment of the present invention isa correlation canceler having a signal input for receiving the firstattenuation signal and a primary reference input for receiving theprimary reference. The correlation canceler produces an output signalhaving a significant component which is derived from the component whichis representative of the signal complicating feature.

The present invention also comprises a method of removing or determininga motion artifact signal from a signal derived from a physiologicalmeasurement wherein a first signal having a physiological measurementcomponent and a motion artifact component and a second signal having aphysiological measurement component and a motion artifact component areacquired. From the first and second signals a secondary reference whichis a primary function of the first and second signals motion artifactcomponents is derived. This method of removing a motion artifact signalfrom a signal derived from a physiological measurement may also comprisethe step of inputting the secondary reference into a correlationcanceler, such as an adaptive noise canceler, to produce an outputsignal which is a significant function of the physiological measurementcomponent of the first or second signal. Alternatively, from the firstand second signals a primary reference which is a significant functionof the physiological measurement components of the first and secondsignals may be derived. This approach may also comprise the step ofinputting the primary reference into a correlation canceler to producean output signal which is a significant function of the first or secondsignal's motion artifact component.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an ideal plethysmographic waveform.

FIG. 2 schematically illustrates the cross-sectional structure of atypical finger.

FIG. 3 illustrates a plethysmographic waveform which includes amotion-induced erratic signal portion.

FIG. 4a illustrates a schematic diagram of a physiological monitor, tocompute primary physiological signals, incorporating a processor of thepresent invention, and a correlation canceler.

FIG. 4b illustrates a schematic diagram of a physiological monitor, tocompute secondary erratic signals, incorporating a processor of thepresent invention, and a correlation canceler.

FIG. 5a illustrates an example of an adaptive noise canceler which couldbe employed in a physiological monitor, to compute primary physiologicalsignals, which also incorporates the processor of the present invention.

FIG. 5b illustrates an example of an adaptive noise canceler which couldbe employed in a physiological monitor, to compute secondary motionartifact signals, which also incorporates the processor of the presentinvention.

FIG. 5c illustrates the transfer function of a multiple notch filter.

FIG. 6a illustrates a schematic absorbing material comprising Nconstituents within an absorbing material.

FIG. 6b illustrates another schematic absorbing material comprising Nconstituents, including one mixed layer, within an absorbing material.

FIG. 6c illustrates another schematic absorbing material comprising Nconstituents, including two mixed layers, within an absorbing material.

FIG. 7a illustrates a schematic diagram of a monitor, to compute primaryand secondary signals, incorporating a processor of the presentinvention, a plurality of signal coefficients ω₁, ω₂, . . . ω_(n), and acorrelation canceler.

FIG. 7b illustrates the ideal correlation canceler energy or poweroutput as a function of the signal coefficients ω₁, ω₂, . . . ω_(n). Inthis particular example, ω₃ =ω_(a) and ω₇ =ω_(v).

FIG. 7c illustrates the non-ideal correlation canceler energy or poweroutput as a function of the signal coefficients ω₁, ω₂, . . . ω_(n). Inthis particular example, ω₃ =ω_(a) and ω₇ =ω_(v).

FIG. 8 is a schematic model of a joint process estimator comprising aleast-squares lattice predictor and a regression filter.

FIG. 9 is a flowchart representing a subroutine capable of implementinga joint process estimator as modeled in FIG. 8.

FIG. 10 is a schematic model of a joint process estimator with aleast-squares lattice predictor and two regression filters.

FIG. 11 is an example of a physiological monitor incorporating aprocessor of the present invention and a correlation canceler within amicroprocessor. This physiological monitor is specifically designed tomeasure a plethysmographic waveform or a motion artifact waveform andperform oximetry measurements.

FIG. 12 is a graph of oxygenated and deoxygenated hemoglobin absorptioncoefficients vs. wavelength.

FIG. 13 is a graph of the ratio of the absorption coefficients ofdeoxygenated hemoglobin divided by oxygenated hemoglobin vs. wavelength.

FIG. 14 is an expanded view of a portion of FIG. 12 marked by a circlelabeled 13.

FIG. 15 illustrates a signal measured at a first red wavelengthλa=λred1=650 nm for use in a processor of the present inventionemploying the ratiometric method for determining either the primaryreference n'(t) or the secondary reference s'(t) and for use in acorrelation canceler, such as an adaptive noise canceler. The measuredsignal comprises a primary portion s.sub.λa (t) and a secondary portionn.sub.λa (t).

FIG. 16 illustrates a signal measured at a second red wavelengthλb=λred2=685 nm for use in a processor of the present inventionemploying the ratiometric method for determining the secondary referencen'(t) or the primary reference s'(t). The measured signal comprises aprimary portion s.sub.λb (t) and a secondary portion n.sub.λb (t).

FIG. 17 illustrates a signal measured at an infrared wavelengthλc=λIR=940 nm for use in a correlation canceler. The measured signalcomprises a primary portion s.sub.λc (t) and a secondary portionn.sub.λc (t).

FIG. 18 illustrates the secondary reference n'(t) determined by aprocessor of the present invention using the ratiometric method.

FIG. 19 illustrates the primary reference s'(t) determined by aprocessor of the present invention using the ratiometric method.

FIG. 20 illustrates a good approximation s".sub.λa (t) to the primaryportion s.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred1=650nm estimated by correlation cancellation with a secondary referencen'(t) determined by the ratiometric method.

FIG. 21 illustrates a good approximation s".sub.λc (t) to the primaryportion s.sub.λc (t) of the signal S.sub.λc (t) measured at λc=λIR=940nm estimated by correlation cancellation with a secondary referencen'(t) determined by the ratiometric method.

FIG. 22 illustrates a good approximation n".sub.λa (t) to the secondaryportion n.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred1=650nm estimated by correlation cancellation with a primary reference s'(t)determined by the ratiometric method.

FIG. 23 illustrates a good approximation n".sub.λc (t) to the secondaryportion n.sub.λc (t) of the signal S.sub.λc (t) measured at λc=λIR=940nm estimated by correlation cancelation with a primary reference s'(t)determined by the ratiometric method.

FIG. 24 illustrates a signal measured at a red wavelength λa=λred=660 nmfor use in a processor of the present invention employing the constantsaturation method for determining the secondary reference n'(t) or theprimary reference s'(t) and for use in a correlation canceler. Themeasured signal comprises a primary portion s.sub.λa (t) and a secondaryportion n.sub.λa (t).

FIG. 25 illustrates a signal measured at an infrared wavelengthλb=λIR=940 nm for use in a processor of the present invention employingthe constant saturation method for determining the secondary referencen'(t) or the primary reference s'(t) and for use in a correlationcanceler. The measured signal comprises a primary portion s.sub.λb (t)and a secondary portion n.sub.λb (t).

FIG. 26 illustrates the secondary reference n'(t) determined by aprocessor of the present invention using the constant saturation method.

FIG. 27 illustrates the primary reference s'(t) determined by aprocessor of the present invention using the constant saturation method.

FIG. 28 illustrates a good approximation s".sub.λa (t) to the primaryportion s.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred=660nm estimated by correlation cancelation with a secondary reference n'(t)determined by the constant saturation method.

FIG. 29 illustrates a good approximation s".sub.λb (t) to the primaryportion s.sub.λb (t) of the signal S.sub.λb (t) measured at λb=λIR=940nm estimated by correlation cancelation with a secondary reference n'(t)determined by the constant saturation method.

FIG. 30 illustrates a good approximation n".sub.λa (t) to the secondaryportion n.sub.λa (t) of the signal S.sub.λa (t) measured at λa=λred=660nm estimated by correlation cancelation with a primary reference s'(t)determined by the constant saturation method.

FIG. 31 illustrates a good approximation n".sub.λb (t) to the secondaryportion n.sub.λa (t) of the signal S.sub.λb (t) measured at λb=λIR=940nm estimated by correlation cancelation with a primary reference s'(t)determined by the constant saturation method.

FIG. 32 depicts a set of 3 concentric electrodes, i.e. a tripolarelectrode sensor, to derive electrocardiography (ECG) signals, denotedas S₁, S₂ and S₃, for use with the present invention. Each of the ECGsignals contains a primary portion and a secondary portion.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is a processor which determines either a secondaryreference n'(t) or a primary reference s'(t) for use in a correlationcanceler, such as an adaptive noise canceler. A correlation canceler mayestimate a good approximation s"(t) to a primary signal s(t) from acomposite signal S(t)=s(t)+n(t) which, in addition to the primaryportion s(t) comprises a secondary portion n(t). It may also be used toprovide a good approximation n"(t) to the secondary signal n(t). Thesecondary portion n(t) may contain one or more of a constant portion, apredictable portion, an erratic portion, a random portion, etc. Theapproximation to the primary signal s"(t) or secondary signal n"(t) isderived by removing as many of the secondary portions n(t) or primaryportions s(t) from the composite signal S(t) as possible. The constantportion and predictable portion are easily removed with traditionalfiltering techniques, such as simple subtraction, low pass, band pass,and high pass filtering. The erratic portion is more difficult to removedue to its unpredictable nature. If something is known about the erraticsignal, even statistically, it could be removed, at least partially,from the measured signal via traditional filtering techniques. However,it is often the case that no information is known about the erraticportion of the noise. In this case, traditional filtering techniques areusually insufficient. Often no information about the erratic portion ofthe measured signal is known. Thus, a correlation canceler, such as anadaptive noise canceler may be utilized in the present invention toremove or derive the erratic portion.

Generally, a correlation canceler has two signal inputs and one output.One of the inputs is either the secondary reference n'(t) or the primaryreference s'(t) which are correlated, respectively, to the secondarysignal portions n(t) and the primary signal portions s(t) present in thecomposite signal S(t). The other input is for the composite signal S(t).Ideally, the output of the correlation canceler s"(t) or n"(t)corresponds, respectively, to the primary signal s(t) or the secondarysignal n(t) portions only. Often, the most difficult task in theapplication of correlation cancelers is determining the referencesignals n'(t) and s'(t) which are correlated to the secondary n(t) andprimary s(t) portions, respectively, of the measured signal S(t) since,as discussed above, these portions are quite difficult to isolate fromthe measured signal S(t). In the signal processor of the presentinvention, either a secondary reference n'(t) or a primary references'(t) is determined from two composite signals measured simultaneously,or nearly simultaneously, at two different wavelengths, λa and λb.

A block diagram of a generic monitor incorporating a signal processor,or reference processor, according to the present invention, and acorrelation canceler is shown in FIGS. 4a and 4b. Two measured signals,S.sub.λa (t) and S.sub.λb (t), are acquired by a detector 20. Oneskilled in the art will realize that for some physiologicalmeasurements, more than one detector may be advantageous. Each signal isconditioned by a signal conditioner 22a and 22b. Conditioning includes,but is not limited to, such procedures as filtering the signals toremove constant portions and amplifying the signals for ease ofmanipulation. The signals are then converted to digital data by ananalog-to-digital converter 24a and 24b. The first measured signalS.sub.λa (t) comprises a first primary signal portion, labeled hereins.sub.λa (t), and a first secondary signal portion, labeled hereinn.sub.λa (t). The second measured signal S.sub.λb (t) is at leastpartially correlated to the first measured signal S.sub.λa (t) andcomprises a second primary signal portion, labeled herein s.sub.λb (t),and a second secondary signal portion, labeled herein n.sub.λb (t).Typically the first and second secondary signal portions, n.sub.λa (t)and n.sub.λb (t), are uncorrelated and/or erratic with respect to theprimary signal portions s.sub.λa (t) and s.sub.λb (t). The secondarysignal portions n.sub.λa (t) and n.sub.λb (t) are often caused by motionof a patient. The signals S.sub.λa (t) and S.sub.λb (t) are input to areference processor 26. The reference processor 26 multiplies the secondmeasured signal S.sub.λb (t) by either a factor ω_(a) =s.sub.λa(t)/s.sub.λb (t) or a factor ω_(v) =n.sub.λa (t)/n.sub.λb (t) and thensubtracts the second measured signal S.sub.λb (t) from the firstmeasured signal S.sub.λa (t). The signal coefficient factors ω_(a) andω_(v) are determined to cause either the primary signal portionss.sub.λa (t) and s.sub.λb (t) or the secondary signal portions n.sub.λa(t) and n.sub.λb (t) to cancel when the two signals S.sub.λa (t) andS.sub.λb (t) are subtracted. Thus, the output of the reference processor26 is either a secondary reference signal n'(t)=n.sub.λa (t)-ω_(a)n.sub.λb (t), in FIG. 4a, which is correlated to both of the secondarysignal portions n.sub.λa (t) and n.sub.λb (t) or a primary referencesignal s'(t)=s.sub.λa (t)-ω_(v) s.sub.λb (t), in FIG. 4b, which iscorrelated to both of the primary signal portions s.sub.λa (t) ands.sub.λb (t). A reference signal n'(t) or s'(t) is input, along with oneof the measured signals S.sub.λa (t) or S.sub.λb (t), to a correlationcanceler 27 which uses the reference signal n'(t) or s'(t) to removeeither the secondary signal portions n.sub.λa (t) or n.sub.λb (t) or theprimary signal portions s.sub.λa (t) or s.sub.λb (t) from the measuredsignal S.sub.λa (t) or S.sub.λb (t). The output of the correlationcanceler 27 is a good approximation s"(t) or n"(t) to either the primarys(t) or the secondary n(t) signal components. The approximation s"(t) orn"(t) is displayed on the display 28.

An adaptive noise canceler 30, an example of which is shown in blockdiagram form in FIG. 5a, is employed to remove either one of theerratic, secondary signal portions n.sub.λa (t) and n.sub.λb (t) fromthe first and second signals S.sub.λa (t) and S.sub.λb (t). The adaptivenoise canceler 30, which performs the functions of a correlationcanceler, in FIG. 5a has as one input a sample of the secondaryreference n'(t) which is correlated to the secondary signal portionsn.sub.λa (t) and n.sub.λb (t). The secondary reference n'(t) isdetermined from the two measured signals S.sub.λa (t) and S.sub.λb (t)by the processor 26 of the present invention as described herein. Asecond input to the adaptive noise canceler, is a sample of either thefirst or second composite measured signals S.sub.λa (t)=s.sub.λa(t)+n.sub.λa (t) or S.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t).

The adaptive noise canceler 30, in FIG. 5b, may also be employed toremove either one of primary signal portions s.sub.λa (t) and s.sub.λb(t) from the first and second signals S.sub.λa (t) and S.sub.λb (t). Theadaptive noise canceler 30 has as one input a sample of the primaryreference s'(t) which is correlated to the primary signal portionss.sub.λa (t) and s.sub.λb (t). The primary reference s'(t) is determinedfrom the two measured signals S.sub.λa (t) and S.sub.λb (t) by theprocessor 26 of the present invention as described herein. A secondinput to the adaptive noise canceler 30 is a sample of either the firstor second measured signals S.sub.λa (t)=s.sub.λa (t)+n.sub.λa (t) orS.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t).

The adaptive noise canceler 30 functions to remove frequencies common toboth the reference n'(t) or s'(t) and the measured signal S.sub.λa (t)or S.sub.λb (t). Since the reference signals are correlated to eitherthe secondary signal portions n.sub.λa (t) and n.sub.λb (t) or theprimary signal portions s.sub.λa (t) and s.sub.λb (t), the referencesignals will be correspondingly erratic or well behaved. The adaptivenoise canceler 30 acts in a manner which may be analogized to a dynamicmultiple notch filter based on the spectral distribution of thereference signal n'(t) or s'(t).

Referring to FIG. 5c, the transfer function of a multiple notch filteris shown. The notches, or dips in the amplitude of the transferfunction, indicate frequencies which are attenuated or removed when acomposite measured signal passes through the notch filter. The output ofthe notch filter is the composite signal having frequencies at which anotch was present removed. In the analogy to an adaptive noise canceler30, the frequencies at which notches are present change continuouslybased upon the inputs to the adaptive noise canceler 30.

The adaptive noise canceler 30 shown in FIGS. 5a and 5b produces anoutput signal, labeled herein as s".sub.λa (t), s".sub.λb (t), n".sub.λa(t) or n".sub.λb (t) which is fed back to an internal processor 32within the adaptive noise canceler 30. The internal processor 32automatically adjusts its own transfer function according to apredetermined algorithm such that the output of the internal processor32, labeled b(t) in FIG. 5a or c(t) in FIG. 5b, closely resembles eitherthe secondary signal portion n.sub.λa (t) or n.sub.λb (t) or the primarysignal portion s.sub.λa (t) or s.sub.λb (t). The output b(t) of theinternal processor 32 in FIG. 5a is subtracted from the measured signal,S.sub.λa (t) or S.sub.λb (t), yielding a signal output s".sub.λa(t)=s.sub.λa (t)+n.sub.λa (t)-b.sub.λa (t) or a signal output s".sub.λb(t)=s.sub.λb (t)+n.sub.λb (t)-b.sub.λb (t). The internal processoroptimizes s".sub.λa (t) or s".sub.λb (t) such that s".sub.λa (t) ors".sub.λb (t) is approximately equal to the primary signal s.sub.λa (t)or s.sub.λb (t), respectively. The output c(t) of the internal processor32 in FIG. 5b is subtracted from the measured signal, S.sub.λa (t) orS.sub.λb (t), yielding a signal output given by n".sub.λa (t)=s.sub.λa(t)+n.sub.λa (t)-c.sub.λa (t) or a signal output given by n".sub.λb(t)=s.sub.λb (t)+n.sub.λb (t)-c.sub.λb (t). The internal processoroptimizes n".sub.λa (t) or n".sub.λb (t) such that n".sub.λa (t) orn".sub.λb (t) is approximately equal to the secondary signal n.sub.λa(t) or n.sub.λb (t), respectively.

One algorithm which may be used for the adjustment of the transferfunction of the internal processor 32 is a least-squares algorithm, asdescribed in Chapter 6 and Chapter 12 of the book Adaptive SignalProcessing by Bernard Widrow and Samuel Stearns, published by PrenticeHall, copyright 1985. This entire book, including Chapters 6 and 12, ishereby incorporated herein by reference.

Adaptive processors 30 in FIGS. 5a and 5b have been successfully appliedto a number of problems including antenna sidelobe canceling, patternrecognition, the elimination of periodic interference in general, andthe elimination of echoes on long distance telephone transmission lines.However, considerable ingenuity is often required to find a suitablereference signal n'(t) or s'(t) since the portions n.sub.λa (t),n.sub.λb (t), s.sub.λa (t) and s.sub.λb (t) cannot easily be separatedfrom the measured signals S.sub.λa (t) and S.sub.λb (t). If either theactual secondary portion n.sub.λa (t) or n.sub.λb (t) or the primarysignal portion s.sub.λa (t) or s.sub.λb (t) were a priori available,techniques such as correlation cancellation would not be necessary. Thedetermination of a suitable reference signal n'(t) or s'(t) frommeasurements taken by a monitor incorporating a reference processor ofthe present invention is one aspect of the present invention.

GENERALIZED DETERMINATION OF PRIMARY AND SECONDARY REFERENCE SIGNALS

An explanation which describes how the reference signals n'(t) and s'(t)may be determined follows. A first signal is measured at, for example, awavelength λa, by a detector yielding a signal S.sub.λa (t):

    s.sub.λa (t)=s.sub.λa (t)+n.sub.λa (t) (1)

where s.sub.λa (t) is the primary signal and n.sub.λa (t) is thesecondary signal.

A similar measurement is taken simultaneously, or nearly simultaneously,at a different wavelength, λb, yielding:

    s.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t). (2)

Note that as long as the measurements, S.sub.λa (t) and S.sub.λb (t),are taken substantially simultaneously, the secondary signal components,n.sub.λa (t) and n.sub.λb (t), will be correlated because any random orerratic functions will affect each measurement in nearly the samefashion. The well behaved primary signal components, s.sub.λa (t) ands.sub.λb (t), will also be correlated to one another.

To obtain the reference signals n'(t) and s'(t), the measured signalsS.sub.λa (t) and S.sub.λb (t) are transformed to eliminate,respectively, the primary or secondary signal components. One way ofdoing this is to find proportionality constants, ω_(a) and ω_(v),between the primary signals s.sub.λa (t) and s.sub.λb (t) and secondarysignals n.sub.λa (t) and n.sub.λb (t) such that:

    s.sub.λa (t)=ω.sub.a s.sub.λb (t)

    n.sub.λa (t)=ω.sub.v n.sub.λb (t).     (3)

These proportionality relationships can be satisfied in manymeasurements, including but not limited to absorption measurements andphysiological measurements. Additionally, in most measurements, theproportionality constants ω_(a) and ω_(v) can be determined such that:

    n.sub.λa (t)≠ω.sub.a n.sub.λb (t)

    s.sub.λa (t)≠ω.sub.v s.sub.λb (t). (4)

Multiplying equation (2) by ω_(a) and then subtracting equation (2) fromequation (1) results in a single equation wherein the primary signalterms s.sub.λa (t) and s.sub.λb (t) cancel, leaving:

    n'(t)=S.sub.λa (t)-ω.sub.a S.sub.λb (t)=n.sub.λa (t)-ω.sub.a n.sub.λb (t);                    (5a)

a non-zero signal which is correlated to each secondary signal portionn.sub.λa (t) and n.sub.λb (t) and can be used as the secondary referencen'(t) in a correlation canceler such as an adaptive noise canceler.

Multiplying equation (2) by ω_(v) and then subtracting equation (2) fromequation (1) results in a single equation wherein the secondary signalterms n.sub.λa (t) and n.sub.λb (t) cancel, leaving:

    s'(t)=S.sub.λa (t)-ω.sub.v S.sub.λb (t)=s.sub.λa (t)-ω.sub.v s.sub.λb (t);                    (5b)

a non-zero signal which is correlated to each of the primary signalportions s.sub.λa (t) and s.sub.λ b(t) and can be used as the signalreference s'(t) in a correlation canceler such as an adaptive noisecanceler.

EXAMPLE OF DETERMINATION OF PRIMARY AND SECONDARY REFERENCE SIGNALS INAN ABSORPTIVE SYSTEM

Correlation canceling is particularly useful in a large number ofmeasurements generally described as absorption measurements. An exampleof an absorption type monitor which can advantageously employcorrelation canceling, such as adaptive noise canceling, based upon areference n'(t) or s'(t) determined by a processor of the presentinvention is one which determines the concentration of an energyabsorbing constituent within an absorbing material when the material issubject to change. Such changes can be caused by forces about whichinformation is desired or primary, or alternatively, by random orerratic secondary forces such as a mechanical force on the material.Random or erratic interference, such as motion, generates secondarycomponents in the measured signal. These secondary components can beremoved or derived by the correlation canceler if a suitable secondaryreference n'(t) or primary reference s'(t) is known.

A schematic N constituent absorbing material comprising a container 42having N different absorbing constituents, labeled A₁, A₂, A₃, . . .A_(N), is shown schematically in FIG. 6a. The constituents A₁ throughA_(N) in FIG. 6a are arranged in a generally orderly, layered fashionwithin the container 42. An example of a particular type of absorptivesystem is one in which light energy passes through the container 42 andis absorbed according to the generalized Beer-Lambert Law of lightabsorption. For light of wavelength λa, this attenuation may beapproximated by:

    I=I.sub.0 exp(-Σ.sup.N.sub.i=1 ε.sub.i, λa c.sub.i x.sub.i)                                                  (6)

Initially transforming the signal by taking the natural logarithm ofboth sides and manipulating terms, the signal is transformed such thatthe signal components are combined by addition rather thanmultiplication, i.e.:

    S.sub.λa =ln(I.sub.0 /I)=Σ.sup.N.sub.i=1 ε.sub.i, λa c.sub.i x.sub.i                                 (7)

where I_(O) is the incident light energy intensity; I is the transmittedlight energy intensity; ε_(i),λa is the absorption coefficient of thei^(th) constituent at the wavelength λa; x_(i) (t) is the optical pathlength of i^(th) layer, i.e., the thickness of material of the i^(th)layer through which optical energy passes; and c_(i) (t) is theconcentration of the i^(th) constituent in the volume associated withthe thickness x_(i) (t). The absorption coefficients ε₁ through ε_(N)are known values which are constant at each wavelength. Mostconcentrations c₁ (t) through c_(N) (t) are typically unknown, as aremost of the optical path lengths x_(i) (t) of each layer. The totaloptical path length is the sum of each of the individual optical pathlengths x_(i) (t) of each layer.

When the material is not subject to any forces which cause change in thethicknesses of the layers, the optical path length of each layer, x_(i)(t), is generally constant. This results in generally constantattenuation of the optical energy and thus, a generally constant offsetin the measured signal. Typically, this portion of the signal is oflittle interest since knowledge about a force which perturbs thematerial is usually desired. Any signal portion outside of a knownbandwidth of interest, including the constant undesired signal portionresulting from the generally constant absorption of the constituentswhen not subject to change, should be removed. This is easilyaccomplished by traditional band pass filtering techniques. However,when the material is subject to forces, each layer of constituents maybe affected by the perturbation differently than each other layer. Someperturbations of the optical path lengths of each layer x_(i) (t) mayresult in excursions in the measured signal which represent desired orprimary information. Other perturbations of the optical path length ofeach layer x_(i) (t) cause undesired or secondary excursions which maskprimary information in the measured signal. Secondary signal componentsassociated with secondary excursions must also be removed to obtainprimary information from the measured signal. Similarly, the ability tocompute secondary signal components caused by secondary excursionsdirectly allows one to obtain primary signal components from themeasured signal via simple subtraction, or correlation cancellationtechniques.

The correlation canceler may selectively remove from the compositesignal, measured after being transmitted through or reflected from theabsorbing material, either the secondary or the primary signalcomponents caused by forces which perturb or change the materialdifferently from the forces which perturbed or changed the material tocause respectively, either the primary or secondary signal component.For the purposes of illustration, it will be assumed that the portion ofthe measured signal which is deemed to be the primary signal s.sub.λa(t) is the attenuation term ε₅ c₅ x₅ (t) associated with a constituentof interest, namely A₅, and that the layer of constituent A₅ is affectedby perturbations different than each of the layers of other constituentsA₁ through A₄ and A₆ through A_(N). An example of such a situation iswhen layer A₅ is subject to forces about which information is deemed tobe primary and, additionally, the entire material is subject to forceswhich affect each of the layers. In this case, since the total forceaffecting the layer of constituent A₅ is different than the total forcesaffecting each of the other layers and information is deemed to beprimary about the forces and resultant perturbation of the layer ofconstituent A₅, attenuation terms due to constituents A₁ through A₄ andA₆ through A_(N) make up the secondary signal portion n.sub.λa (t). Evenif the additional forces which affect the entire material cause the sameperturbation in each layer, including the layer of A₅, the total forceson the layer of constituent A₅ cause it to have different totalperturbation than each of the other layers of constituents A₁ through A₄and A₆ through A_(N).

It is often the case that the total perturbation affecting the layersassociated with the secondary signal components is caused by random orerratic forces. This causes the thickness of layers to changeerratically and the optical path length of each layer, x_(i) (t), tochange erratically, thereby producing a random or erratic secondarysignal component n.sub.λa (t). However, regardless of whether or not thesecondary signal portion n.sub.λa (t) is erratic, the secondary signalcomponent n.sub.λa (t) can be either removed or derived via acorrelation canceler, such as an adaptive noise canceler, having as oneinput, respectively, a secondary reference n'(t) or a primary references'(t) determined by a processor of the present invention as long as theperturbation on layers other than the layer of constituent A₅ isdifferent than the perturbation on the layer of constituent A₅. Thecorrelation canceler yields a good approximation to either the primarysignal s.sub.λa (t) or the secondary signal n.sub.λa (t). In the eventthat an approximation to the primary signal is obtained, theconcentration of the constituent of interest, c₅ (t), can often bedetermined since in some physiological measurements, the thickness ofthe primary signal component, x₅ (t) in this example, is known or can bedetermined.

The correlation canceler utilized a sample of either the secondaryreference n'(t) or the primary reference s'(t) determined from twosubstantially simultaneously measured signals S.sub.λa (t) and S.sub.λb(t). S.sub.λa (t) is determined as above in equation (7). S.sub.λb (t)is determined similarly at a different wavelength λb. To find either thesecondary reference n'(t) or the primary reference s'(t), attenuatedtransmitted energy is measured at the two different wavelengths λa andλb and transformed via logarithmic conversion. The signals S.sub.λa (t)and S.sub.λb (t) can then be written (logarithm converted) as:

    S.sub.λa (t)=ε.sub.5,λa c.sub.5 x.sub.5 (t)+Σ.sup.4.sub.i=1 ε.sub.i,λa c.sub.i x.sub.i +Σ.sup.N.sub.i=6 ε.sub.i,λa c.sub.i x.sub.i (8)

    S.sub.λa (t)=ε.sub.5,λa c.sub.5 x.sub.5 (t)+n.sub.λa (t)                                   (9)

    S.sub.λb (t)=ε.sub.5,λb c.sub.5 x.sub.5 (t)+Σ.sup.4.sub.i=1 ε.sub.i,λb c.sub.i x.sub.i +Σ.sup.N.sub.i=6 ε.sub.i,λb c.sub.i x.sub.i (10)

    S.sub.λb (t)=ε.sub.5,λb c.sub.5 x.sub.5 (t)+n.sub.λb (t)                                   (11)

Further transformations of the signals are the proportionalityrelationships defining ω_(a) and ω_(v), similarly to equation (3), whichallows determination of a noise reference n'(t) and a primary references'(t). These are:

    ε.sub.5, λa =ω.sub.a ε.sub.5, λb (12a)

    n.sub.λa =ω.sub.v n.sub.λb             (12b)

where

    n.sub.λa ≠ω.sub.a n.sub.λb       (13a)

    ε.sub.5, λa ≠ω.sub.v ε.sub.5, λb. (13b)

It is often the case that both equations (12) and (13) can besimultaneously satisfied. Multiplying equation (11) by ω_(a) andsubtracting the result from equation (9) yields a non-zero secondaryreference which is a linear sum of secondary signal components: ##EQU1##

Multiplying equation (11) by ω_(v) and subtracting the result fromequation (9) yields a primary reference which is a linear sum of primarysignal components: ##EQU2##

A sample of either the secondary reference n'(t) or the primaryreference s'(t), and a sample of either measured signal S.sub.λa (t) orS.sub.λb (t), are input to a correlation canceler 27, such as anadaptive noise canceler 30, an example of which is shown in FIGS. 5a and5b and a preferred example of which is discussed herein under theheading PREFERRED CORRELATION CANCELER USING A JOINT PROCESS ESTIMATORIMPLEMENTATION. The correlation canceler 27 removes either the secondaryportion n.sub.λa (t) or n.sub.λb (t), or the primary portions, s.sub.λa(t) or s.sub.λb (t), of the measured signal yielding a goodapproximation to either the primary signals s".sub.λa (t)≈ε₅,λa c₅ x₅(t) or s".sub.λb (t)≈ε₅,λb c₅ x₅ (t) or the secondary signals n".sub.λa(t)≈n.sub.λa (t) or n".sub.λb (t)≈n.sub.λb (t). In the event that theprimary signals are obtained, the concentration c₅ (t) may then bedetermined from the approximation to the primary signal s".sub.λa (t) ors".sub.λb (t) according to:

    c.sub.5 (t)≈s".sub.λa (t)/ε.sub.5,λa x.sub.5 (t)≈s".sub.λb (t)/ε.sub.5,λb x.sub.5 (t). (17)

As discussed previously, the absorption coefficients are constant ateach wavelength λa and λb and the thickness of the primary signalcomponent, x₅ (t) in this example, is often known or can be determinedas a function of time, thereby allowing calculation of the concentrationc₅ (t) of constituent A₅.

DETERMINATION OF CONCENTRATION OR SATURATION IN A VOLUME CONTAINING MORETHAN ONE CONSTITUENT

Referring to FIG. 6b, another material having N different constituentsarranged in layers is shown. In this material, two constituents A₅ andA₆ are found within one layer having thickness x₅,6 (t)=x₅ (t)+x₆ (t),located generally randomly within the layer. This is analogous tocombining the layers of constituents A₅ and A₆ in FIG. 6a. A combinationof layers, such as the combination of layers of constituents A₅ and A₆,is feasible when the two layers are under the same total forces whichresult in the same change of the optical path lengths x₅ (t) and x₆ (t)of the layers.

Often it is desirable to find the concentration or the saturation, i.e.,a percent concentration, of one constituent within a given thicknesswhich contains more than one constituent and is subject to uniqueforces. A determination of the concentration or the saturation of aconstituent within a given volume may be made with any number ofconstituents in the volume subject to the same total forces andtherefore under the same perturbation or change. To determine thesaturation of one constituent in a volume comprising many constituents,as many measured signals as there are constituents which absorb incidentlight energy are necessary. It will be understood that constituentswhich do not absorb light energy are not consequential in thedetermination of saturation. To determine the concentration, as manysignals as there are constituents which absorb incident light energy arenecessary as well as information about the sum of concentrations.

It is often the case that a thickness under unique motion contains onlytwo constituents. For example, it may be desirable to know theconcentration or saturation of A₅ within a given volume which containsA₅ and A₆. In this case, the primary signals s.sub.λa (t) and s.sub.λb(t) comprise terms related to both A₅ and A₆ so that a determination ofthe concentration or saturation of A₅ or A₆ in the volume may be made. Adetermination of saturation is discussed herein. It will be understoodthat the concentration of A₅ in a volume containing both A₅ and A₆ couldalso be determined if it is known that A₅ +A₆ =1, i.e., that there areno constituents in the volume which do not absorb incident light energyat the particular measurement wavelengths chosen. The measured signalsS.sub.λa (t) and S.sub.λb (t) can be written (logarithm converted) as:##EQU3##

It is also often the case that there may be two or more thicknesseswithin a medium each containing the same two constituents but eachexperiencing a separate motion as in FIG. 6c. For example, it may bedesirable to know the concentration or saturation of A₅ within a givenvolume which contains A₅ and A₆ as well as the concentration orsaturation of A₃ within a given volume which contains A₃ and A₄, A₃ andA₄ having the same constituency as A₅ and A₆, respectively. In thiscase, the primary signals s.sub.λa (t) and s.sub.λb (t) again compriseterms related to both A₅ and A₆ and portions of the secondary signalsn.sub.λa (t) and n.sub.λb (t) comprise terms related to both A₃ and A₄.The layers, A₃ and A₄, do not enter into the primary equation becausethey are assumed to be perturbed by random or erratic secondary forceswhich are uncorrelated with the primary forge. Since constituents 3 and5 as well as constituents 4 and 6 are taken to be the same, they havethe same absorption coefficients. i.e. ε₃, λa =ε₅, λa, ε₃, λb =ε₅, λb,ε₄, λa = ε₆, λa and ε₄, λb =ε₆, λb. Generally speaking, however, A₃ andA₄ will have different concentrations than A₅ and A₆ and will thereforehave a different saturation. Consequently a single constituent within amedium may have one or more saturations associated with it. The primaryand secondary signals according to this model may be written as:

    s.sub.λa (t)=[ε.sub.5, λa c.sub.5 +ε.sub.6, λa c.sub.6 ]x.sub.5, 6 (t)                         (20a)

    n.sub.λa (t)=[ε.sub.5, λa c.sub.3 +ε.sub.6, λa c.sub.4 ]x.sub.3, 4 (t) +Σ.sup.2 .sub.i=1 ε.sub.i,λa c.sub.i x.sub.i (t)+Σ.sup.N.sub.i=7 ε.sub.i,λa c.sub.i x.sub.i (t)             (20b)

    n.sub.λa (t)=[ε.sub.5, λa c.sub.3 +ε.sub.6, λa c.sub.4 ]x.sub.3, 4 (t)+n.sub.λa (t)     (20c)

    s.sub.λb (t)=[ε.sub.5, λb c.sub.5 +ε.sub.6, λb c.sub.6 ]x.sub.5, 6 (t)                         (21a)

    n.sub.λb (t)=[ε.sub.5, λb c.sub.3 +ε.sub.6, λb c.sub.4 ]x.sub.3, 4 (t) +Σ.sup.2 .sub.i=1 ε.sub.i,λb c.sub.i x.sub.i (t)+Σ.sup.N.sub.i=7 ε.sub.i,λb c.sub.i x.sub.i (t).            (21b)

    n.sub.λb (t)=[ε.sub.5, λb c.sub.3 +ε.sub.6, λa c.sub.4 ]x.sub.3, 4 (t)+n.sub.λb (t)     (21c)

where signals n.sub.λa (t) and n.sub.λb (t) are similar to the secondarysignals n.sub.λa (t) and n.sub.λb (t) except for the omission of the 3,4 layer.

Any signal portions whether primary or secondary, outside of a knownbandwidth of interest, including the constant undesired secondary signalportion resulting from the generally constant absorption of theconstituents when not under perturbation, should be removed to determinean approximation to either the primary signal or the secondary signalwithin the bandwidth of interest. This is easily accomplished bytraditional band pass filtering techniques. As in the previous example,it is often the case that the total perturbation or change affecting thelayers associated with the secondary signal components is caused byrandom or erratic forces, causing the thickness of each layer, or theoptical path length of each layer, x_(i) (t), to change erratically,producing a random or erratic secondary signal component n.sub.λa (t).Regardless of whether or not the secondary signal portion n.sub.λa (t)is erratic, the secondary signal component n.sub.λa (t) can be removedor derived via a correlation canceler, such as an adaptive noisecanceler, having as one input a secondary reference n'(t) or a primaryreference s'(t) determined by a processor of the present invention aslong as the perturbation in layers other than the layer of constituentsA₅ and A₆ is different than the perturbation in the layer ofconstituents A₅ and A₆. Either the erratic secondary signal componentsn.sub.λa (t) and n.sub.λb (t) or the primary components s.sub.λa (t) ands.sub.λb (t) may advantageously be removed from equations (18) and (19),or alternatively equations (20) and (21), by a correlation canceler. Thecorrelation canceler, again, requires a sample of either the primaryreference s'(t) or the secondary reference n'(t) and a sample of eitherof the composite signals S.sub.λa (t) or S.sub.λb (t) of equations (18)and (19).

DETERMINATION OF PRIMARY AND SECONDARY REFERENCE SIGNALS FOR SATURATIONMEASUREMENTS

Two methods which may be used by a processor of the present invention todetermine either the secondary reference n'(t) or the primary references'(t) are a ratiometric method and a constant saturation method. Oneembodiment of a physiological monitor incorporating a processor of thepresent invention utilizes the ratiometric method wherein the twowavelengths λa and λb, at which the signals S.sub.λa (t) and S.sub.λb(t) are measured, are specifically chosen such that a relationshipbetween the absorption coefficients ε₅,λa, ε₅,λb, ε₆,λa and ε₆,λbexists, i.e.:

    ε.sub.5,λa /ε.sub.6,λa =ε.sub.5,λb /ε.sub.6,λb     (22)

The measured signals S.sub.λa (t) and S.sub.λb (t) can be factored andwritten as:

    S.sub.λa (t)=ε.sub.6,λa [(ε.sub.5,λa /ε.sub.6,λa)c.sub.5 x.sub.5,6 (t)+c.sub.6 x.sub.5,6 (t)]+n.sub.λa (t)                                  (23a)

    S.sub.λa (t)=ε.sub.6,λa [(ε.sub.5,λa /ε.sub.6,λa)c.sub.5 x.sub.5,6 (t)+c.sub.6 x.sub.5,6 (t) +(ε.sub.5,λa /ε.sub.6,λa)c.sub.3 x.sub.3,4 (t)+c.sub.4 x.sub.3,4 (t)]+n.sub.λa (t)            (23b)

    S.sub.λa (t)=s.sub.λa +n.sub.λa (t)   (23c)

    S.sub.λb (t)=ε.sub.6,λb [(ε.sub.5,λb /ε.sub.6,λb)c.sub.5 x.sub.5,6 (t)+c.sub.6 x.sub.5,6 (t)]+n.sub.λb (t)                                  (24a)

    S.sub.λb (t)=ε.sub.6,λb [(ε.sub.5,λb /ε.sub.6,λb)c.sub.5 x.sub.5,6 (t)+c.sub.6 x.sub.5,6 (t) +(ε.sub.5,λb /ε.sub.6,λb)c.sub.3 x.sub.3,4 (t)+c.sub.4 x.sub.3,4 (t)]+n.sub.λb (t)            (24b)

    S.sub.λb (t)=s.sub.λb +n.sub.λb (t).  (24c)

The wavelengths λa and λb, chosen to satisfy equation (22), cause theterms within the square brackets to be equal, thereby causing the termsother than n.sub.λa (t) and n.sub.λb (t) to be linearly dependent. Then,proportionality constants ω_(av) and ω_(e) may be found for thedetermination of a non-zero primary and secondary reference

    ε.sub.6, λa =ω.sub.av ε.sub.6, λb (25a)

    n.sub.λa (t)=ω.sub.e n.sub.λb (t)      (25b)

    ε.sub.6, λa ≠ω.sub.e ε.sub.6, λb (26a)

    n.sub.λa (t)≠ω.sub.av n.sub.λb (t) (26b)

It is often the case that both equations (25) and (26) can besimultaneously satisfied. Additionally, since the absorptioncoefficients of each constituent are constant with respect towavelength, the proportionality constants ω_(av) and ω_(e) can be easilydetermined. Furthermore, absorption coefficients of other constituentsA₁ through A₂ and A₇ through A_(N) are generally unequal to theabsorption coefficients of A₃, A₄, A₅ and A₆. Thus, the secondarycomponents n.sub.λa and n.sub.λb are generally not made linearlydependent by the relationships of equations (22) and (25).

Multiplying equation (24) by ω_(av) and subtracting the resultingequation from equation (23), a non-zero secondary reference isdetermined by: ##EQU4## Multiplying equation (24) by ω_(e) andsubtracting the resulting equation from equation (23), a non-zeroprimary reference is determined by: ##EQU5##

An alternative method for determining reference signals from themeasured signals S.sub.λa (t) and S.sub.λb (t) using a processor of thepresent invention is the constant saturation approach. In this approach,it is assumed that the saturation of A₅ in the volume containing A₅ andA₆ and the saturation of A₃ in the volume containing A₃ and A₄ remainsrelatively constant over some period of time, i.e.:

    Saturation(A.sub.5 (t))=c.sub.5 (t)/[c.sub.5 (t)+c.sub.6 (t)](28a)

    Saturation(A.sub.3 (t))=c.sub.3 (t)/[c.sub.3 (t)+c.sub.4 (t)](28b)

    Saturation(A.sub.5 (t))={1+[c.sub.6 (t)/c.sub.5 (t)]}.sup.-1 (29a)

    Saturation(A.sub.3 (t))={1+[c.sub.4 (t)/c.sub.3 (t)]}.sup.-1 (29b)

are substantially constant over many samples of the measured signalsS.sub.λa and S.sub.λb. This assumption is accurate over many samplessince saturation generally changes relatively slowly in physiologicalsystems.

The constant saturation assumption is equivalent to assuming that:

    c.sub.5 (t)/c.sub.6 (t)=constant.sub.1                     (30a)

    c.sub.3 (t)/c.sub.4 (t)=constant.sub.2                     (30b)

since the only other term in equations (29a) and (29b) is a constant,namely the numeral 1.

Using this assumption, the proportionality constants ω_(a) and ω_(v)which allow determination of the secondary reference signal n'(t) andthe primary reference signal s'(t) in the constant saturation methodare: ##EQU6##

It is often the case that both equations (32) and (36) can besimultaneously satisfied to determine the proportionality constantsω_(a) and ω_(v). Additionally, the absorption coefficients at eachwavelength ε₅,λa, ε₆,λa, ε₅,λb, and ε₆,λb are constant and the centralassumption of the constant saturation method is that c₅ (t)/c₆ (t) andc₃ (t)/c₄ (t) are constant over many sample periods. Thus, newproportionality constants ω_(a) and ω_(v) may be determined every fewsamples from new approximations to either the primary or secondarysignal as output from the correlation canceler. Thus, the approximationsto either the primary signals s.sub.λa (t) and s.sub.λb (t) or thesecondary signals n.sub.λa (t) and n.sub.λb (t), found by thecorrelation canceler for a substantially immediately preceding set ofsamples of the measured signals S.sub.λa (t) and S.sub.λb (t) are usedin a processor of the present invention for calculating theproportionality constants, ω_(a) and ω_(v), for the next set of samplesof the measured signals S.sub.λa (t) and S.sub.λb (t).

Multiplying equation (19) by ω_(a) and subtracting the resultingequation from equation (18) yields a non-zero secondary referencesignal:

    n'(t)=S.sub.λa (t)-ω.sub.a S.sub.λb (t)=n.sub.λa (t)-ω.sub.a n.sub.λb (t).                    (37a)

Multiplying equation (19) by ω_(v) and subtracting the resultingequation from equation (18) yields a non-zero primary reference signal:

    s'(t)=S.sub.λa (t)-ω.sub.v S.sub.λb (t)=s.sub.λa (t)-ω.sub.v s.sub.λb (t).                    (37b)

When using the constant saturation method, it is not necessary for thepatient to remain motionless for a short period of time such that anaccurate initial saturation value can be determined by known methodsother than correlation canceling. With no erratic, motion-induced signalportions, a physiological monitor can very quickly produce an initialvalue of the saturation of A₅ in the volume containing A₅ and A₆. Anexample of a saturation calculation is given in the article"SPECTROPHOTOMETRIC DETERMINATION OF OXYGEN SATURATION OF BLOODINDEPENDENT OF THE PRESENT OF INDOCYANINE GREEN" by G. A. Mook, et al.,wherein determination of oxygen saturation in arterial blood isdiscussed. Another article discussing the calculation of oxygensaturation is "PULSE OXIMETRY: PHYSICAL PRINCIPLES, TECHNICALREALIZATION AND PRESENT LIMITATIONS" by Michael R. Neuman. Then, withvalues for the coefficients ω_(a) and ω_(v) determined, a correlationcanceler may be utilized with a secondary reference n'(t) or a primaryreference s'(t) determined by the constant saturation method.

DETERMINATION OF SIGNAL COEFFICIENTS FOR PRIMARY AND SECONDARY REFERENCESIGNALS USING THE CONSTANT SATURATION METHOD

The reference processor 26 of FIG. 4a and FIG. 4b of the presentinvention may be configured to multiply the second measured signalS.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t) by a plurality of signalcoefficients ω₁, ω₂, . . . ω_(n) and then subtract each result from thefirst measured signal S.sub.λa (t)=s.sub.λa (t)+n.sub.λa (t) to obtain aplurality of reference signals

    r'(ω, t)=s.sub.λa (t)-ωs.sub.λb (t)+n.sub.λa (t)-ωn.sub.λb (t)        (38)

for ω=ω₁, ω₂, . . . ω_(n) as shown in FIG. 7a.

In order to determine either the primary reference s'(t) or thesecondary reference n'(t) from the above plurality of reference signalsof equation (38), signal coefficients ω_(a) and ω_(v) must be determinedfrom the plurality of signal coefficients ω₁, ω₂, . . . ω_(n). Thecoefficients ω_(a) and ω_(v) are such that they cause either the primarysignal portions s.sub.λa (t) and s.sub.λb (t) or the secondary signalportions n.sub.λa (t) and n.sub.λb (t) to cancel or nearly cancel whenthey are substituted into the reference function r'(ω, t), e.g.

    s.sub.λa (t)=ω.sub.a s.sub.λb (t)      (39a)

    n.sub.λa (t)=ω.sub.v n.sub.λb (t)      (39b)

    n'(t)=r'(ω.sub.a, t)=n.sub.λa (t)-ω.sub.a n.sub.λb (t)                                       (39c)

    s'(t)=r'(ω.sub.v, t)=s.sub.λa (t)-ω.sub.v s.sub.λb (t).                                      (39d)

In practice, one does not usually have significant prior informationabout either the primary signal portions s.sub.λa (t) and s.sub.λb (t)or the secondary signal portions n.sub.λa (t) and n.sub.λb (t) of themeasured signals S.sub.λa (t) and S.sub.λb (t). The lack of thisinformation makes it difficult to determine which of the plurality ofcoefficients ω₁, ω₂, . . . ω_(n) correspond to the signal coefficientsω_(a) =s.sub.λa (t)/s.sub.λb (t) and ω_(v) =n.sub.λa (t)/n.sub.λb (t).Herein the preferred approach to determine the signal coefficients ω_(a)and ω_(v) from the plurality of coefficients ω₁, ω₂, . . . ω_(n) employsthe use of a correlation canceler 27, such as an adaptive noisecanceler, which takes a first input which corresponds to one of themeasured signals S.sub.λa (t) or S.sub.λb (t) and takes a second inputwhich corresponds to successively each one of the plurality of referencesignals r'(ω₁, t), r'(ω₂, t), . . . , r'(ω_(n), t) as shown in FIG. 7a.For each of the reference signals r'(ω₁, t), r'(ω₂, t), . . . ,r'(ω_(n), t) the corresponding output of the correlation canceler 27 isinput to an integrator 29 for forming a cumulative output signal. Thecumulative output signal is subsequently input to an extremum detector31. The purpose of the extremum detector 31 is to chose signalcoefficients ω_(a) and ω_(v) from the set ω₁, ω₂, . . . ω_(n) byobserving which provide a maximum in the cumulative output signal as inFIGS. 7b and 7c. In other words, coefficients which provide a maximumintegrated output, such as energy or power, from the correlationcanceler 27 correspond to the signal coefficients ω_(a) and ω_(v). Onecould also configure a system geometry which would require one to locatethe coefficients from the set ω₁, ω₂, . . . ω _(n) which provide aminimum or inflection in the cumulative output signal to identify thesignal coefficients ω_(a) and ω_(v).

Use of a plurality of coefficients in the processor of the presentinvention in conjunction with a correlation canceler 27 to determine thesignal coefficients ω_(a) and ω_(v) may be demonstrated by using theproperties of correlation cancellation. If x, y and z are taken to beany collection of three time varying signals, then the properties of ageneric correlation canceler C(x, y) may be defined as follows:

    Property (1) C(x, y)=0 for x, y correlated

    Property (2) C(x, y)=x for x, y uncorrelated               (40)

    Property (3) C(x+y, z)=C(x, z)+C(y, z).

With properties (1), (2) and (3) it is easy to demonstrate that theenergy or power output of a correlation canceler with a first inputwhich corresponds to one of the measured signals S.sub.λa (t) orS.sub.λb (t) and a second input which corresponds to successively eachone of a plurality of reference signals r'(ω₁, t), r'(ω₂, t), . . . ,r'(ω_(n), t) can determine the signal coefficients ω_(a) and ω_(v)needed to produce the primary reference s'(t) and secondary referencen'(t). If we take as a first input to the correlation canceler themeasured signal S.sub.λa (t) and as a second input the plurality ofreference signals r'(ω₁, t), r'(ω₂, t), . . . , r'(ω_(n), t) then theoutputs of the correlation canceler C(S.sub.λa (t),r'(ω_(j),t)) for j=1,2, . . . , n may be written as

    C(s.sub.λa (t)+n.sub.λa (t),s.sub.λa (t)-ω.sub.j s.sub.λb (t)+n.sub.λa (t)-ω.sub.j n.sub.λb (t)) (41)

where j=1, 2, . . . , n and we have used the expressions

    r'(ω, t)=S.sub.λa (t)-ωS.sub.λb (t) (42)

    S.sub.λa (t)=s.sub.λa (t)+n.sub.λa (t) (43a)

    S.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t). (43b)

The use of property (3) allows one to expand equation (41) into twoterms

    C(S.sub.λa (t),r'(ω,t))=C(s.sub.λa (t),s.sub.λa (t)-ωs.sub.λb (t)+n.sub.λa (t)-ωn.sub.λb (t)) +C(n.sub.λa (t),s.sub.λa (t)-ωs.sub.λb (t)+n.sub.λa (t)-ωn.sub.λb (t) )      (44)

so that upon use of properties (1) and (2) the correlation canceleroutput is given by

    C(S.sub.λa (t), r'(ω.sub.j,t))=s.sub.λa (t)δ(ω.sub.j -ω.sub.a)+n.sub.λa (t)δ(ω.sub.j -ω.sub.v)                  (45)

where δ(x) is the unit impulse function

    δ(x)=0 if x≠0

    δ(x)=1 if x=0.                                       (46)

The time variable, t, of the correlation canceler output C(S.sub.λa (t),r'(ω_(j), t)) may be eliminated by computing its energy or power. Theenergy of the correlation canceler output is given by ##EQU7## It mustbe understood that one could, equally well, have chosen the measuredsignal S.sub.λb (t) as the first input to the correlation canceler andthe plurality of reference signals r'(ω₁, t), r'(ω₂, t), . . . ,r'(ω_(n), t) as the second input. In this event, the correlationcanceler energy output is ##EQU8## It must also be understood that inpractical situations the use of discrete time measurement signals may beemployed as well as continuous time measurement signals. In the eventthat discrete time measurement signals are used integrationapproximation methods such as the trapezoid rule, midpoint rule, Tick'srule, Simpson's approximation or other techniques may be used to computethe correlation canceler energy or power output. In the discrete timemeasurement signal case, the energy output of the correlation cancelermay be written, using the trapezoid rule, as

    E.sub.λa (ω)=δ(ω-ω.sub.a)Δt{Σ.sup.n.sub.i=0 s.sup.2.sub.λa (t.sub.i)-0.5(s.sup.2.sub.λa (t.sub.0)+s.sup.2.sub.λa (t.sub.n))}+δ(ω-ω.sub.v)Δt{Σ.sup.n.sub.i=0 n.sup.2.sub.λa (t.sub.i)-0.5(n.sup.2.sub.λa (t.sub.0)+n.sup.2.sub.λa (t.sub.n))}               (48a)

    E.sub.λb (ω)=δ(ω-ω.sub.a)Δt{Σ.sup.n.sub.i=0 s.sup.2.sub.λb (t.sub.i)-0.5(s.sup.2.sub.λb (t.sub.0)+s.sup.2.sub.λb (t.sub.n))}+δ(ω-ω.sub.v)Δt{Σ.sup.n.sub.i=0 n.sup.2.sub.λb (t.sub.i)-0.5(n.sup.2.sub.λb (t.sub.0)+n.sup.2.sub.λb (t.sub.n))}               (48b)

where t_(i) is the i^(th) discrete time, t₀ is the initial time, t_(n)is the final time and Δt is the time between discrete time measurementsamples.

The energy functions given above, and shown in FIG. 7b, indicate thatthe correlation canceler output is usually zero due to correlationbetween the measured signal S.sub.λa (t) or S.sub.λb (t) and many of theplurality of reference signals r'(ω₁, t), r'(ω₂, t), . . . , r'(ω_(n),t)r'(ω, t). However, the energy functions are non zero at values ofω_(j) which correspond to cancellation of either the primary signalportions s.sub.λa (t) and s.sub.λb (t) or the secondary signal portionsn.sub.λa (t) and n.sub.λb (t) in the reference signal r'(ω_(j), t).These values correspond to the signal coefficients ω_(a) and ω_(v).

It must be understood that there may be instances in time when eitherthe primary signal portions s.sub.λa (t) and s.sub.λb (t) or thesecondary signal portions n.sub.λa (t) and n.sub.λb (t) are identicallyzero or nearly zero. In these cases, only one signal coefficient valuewill provide maximum energy or power output of the correlation canceler.

Since there may be more than one signal coefficient value which providesmaximum correlation canceler energy or power output, an ambiguity mayarise. It may not be immediately obvious which signal coefficienttogether with the reference function r'(ω, t) provides either theprimary or secondary reference. In such cases, it is necessary toconsider the constraints of the physical system at hand. For example, inpulse oximetry, it is known that arterial blood, whose signature is theprimary plethysmographic wave, has greater oxygen saturation than venousblood, whose signature is the secondary erratic or random signal.Consequently, in pulse oximetry, the ratio of the primary signals due toarterial pulsation ω_(a) =s.sub.λa (t)/s.sub.λb (t) is the smaller ofthe two signal coefficient values while the ratio of the secondarysignals due to mainly venous blood dynamics ω_(v) =n.sub.λa (t)/n.sub.λb(t) is the larger of the two signal coefficient values, assuming λa=660nm and λb=940 nm.

It must be understood that in practical implementations of the pluralityof reference signals and cross correlator technique, the ideal featureslisted as properties (1), (2) and (3) above will not be preciselysatisfied but will be approximations thereof. Therefore, in practicalimplementations of the present invention, the correlation cancelerenergy curves depicted in FIG. 7b will not consist of infinitely narrowdelta functions but will have finite width associated with them asdepicted in FIG. 7c.

It should also be understood that it is possible to have more than twosignal coefficient values which produce maximum energy or power outputfrom a correlation canceler. This situation will arise when the measuredsignals each contain more than two components each of which are relatedby a ratio as follows:

    S.sub.λa (t)=Σ.sup.n.sub.i=1 f.sub.λa, i (t)

    S.sub.λb (t)=Σ.sup.n.sub.i=1 f.sub.λb, i (t) (49)

where

    f.sub.λa, i (t)=ω.sub.i f.sub.λa, i (t) i=1, . . . , n

    ω.sub.i ≠ω.sub.j.

The ability to employ reference signal techniques together with acorrelation cancellation, such as an adaptive noise canceler, todecompose a signal into two or more signal components each of which isrelated by a ratio is a further aspect of the present invention.

PREFERRED CORRELATION CANCELER USING A JOINT PROCESS ESTIMATORIMPLEMENTATION

Once either the secondary reference n'(t) or the primary reference s'(t)is determined by the processor of the present invention using either theabove described ratiometric or constant saturation methods, thecorrelation canceler can be implemented in either hardware or software.The preferred implementation of a correlation canceler is that of anadaptive noise canceler using a joint process estimator.

The least mean squares (LMS) implementation of the internal processor 32described above in conjunction with the adaptive noise canceler of FIG.5a and FIG. 5b is relatively easy to implement, but lacks the speed ofadaptation desirable for most physiological monitoring applications ofthe present invention. Thus, a faster approach for adaptive noisecanceling, called a least-squares lattice joint process estimator model,is preferably used. A joint process estimator 60 is showndiagrammatically in FIG. 8 and is described in detail in Chapter 9 ofAdaptive Filter Theory by Simon Haykin, published by Prentice-Hall,copyright 1986. This entire book, including Chapter 9, is herebyincorporated herein by reference. The function of the joint processestimator is to remove either the secondary signal portions n.sub.λa (t)or n.sub.λb (t) or the primary signal portions s.sub.λa (t) or s.sub.λb(t) from the measured signals S.sub.λa (t) or S.sub.λb (t), yieldingeither a signal s".sub.λa (t) or s".sub.λb (t) or a signal n".sub.λa (t)or n".sub.λb (t) which is a good approximation to either the primarysignal s.sub.λa (t) or s.sub.λb (t) or the secondary signal n.sub.λa (t)or n.sub.λb (t). Thus, the joint process estimator estimates either thevalue of the primary signals s.sub.λa (t) or s.sub.λb (t) or thesecondary signals n.sub.λa (t) or n.sub.λb (t). The inputs to the jointprocess estimator 60 are either the secondary reference n'(t) or theprimary reference s'(t) and the composite measured signal S.sub.λa (t)or S.sub.λb (t). The output is a good approximation to the signalS.sub.λa (t) or S.sub.λb (t) with either the secondary signal or theprimary signal removed, i.e. a good approximation to either s.sub.λa(t), s.sub.λb (t), n.sub.λa (t) or n.sub.λa (t).

The joint process estimator 60 of FIG. 8 utilizes, in conjunction, aleast square lattice predictor 70 and a regression filter 80. Either thesecondary reference n'(t) or the primary reference s'(t) is input to theleast square lattice predictor 70 while the measured signal S.sub.λa (t)or S.sub.λb (t) is input to the regression filter 80. For simplicity inthe following description, S.sub.λa (t) will be the measured signal fromwhich either the primary portion s.sub.λa (t) or the secondary portionn.sub.λa (t) will be estimated by the joint process estimator 60.However, it will be noted that S.sub.λb (t) could equally well be inputto the regression filter 80 and the primary portion s.sub.λb (t) or thesecondary portion n.sub.λb (t) of this signal could equally well beestimated.

The joint process estimator 60 removes all frequencies that are presentin both the reference n'(t) or s'(t), and the measured signal S.sub.λa(t). The secondary signal portion n.sub.λa (t) usually comprisesfrequencies unrelated to those of the primary signal portion s.sub.λa(t). It is highly improbable that the secondary signal portion n.sub.λa(t) would be of exactly the same spectral content as the primary signalportion s.sub.λa (t). However, in the unlikely event that the spectralcontent of s.sub.λa (t) and n.sub.λa (t) are similar, this approach willnot yield accurate results. Functionally, the joint process estimator 60compares the reference input signal n'(t) or s'(t), which is correlatedto either the secondary signal portion n.sub.λa (t) or the primarysignal portion s.sub.λa (t), and input signal S.sub.λa (t) and removesall frequencies which are identical. Thus, the joint process estimator60 acts as a dynamic multiple notch filter to remove those frequenciesin the secondary signal component n.sub.λa (t) as they changeerratically with the motion of the patient or those frequencies in theprimary signal component s.sub.λa (t) as they change with the arterialpulsation of the patient. This yields a signal having substantially thesame spectral content and amplitude as either the primary signals.sub.λa (t) or the secondary signal n.sub.λa (t). Thus, the outputs".sub.λa (t) or n".sub.λa (t) of the joint process estimator 60 is avery good approximation to either the primary signal s.sub.λa (t) or thesecondary signal n.sub.λa (t).

The joint process estimator 60 can be divided into stages, beginningwith a zero-stage and terminating in an m^(th) -stage, as shown in FIG.8. Each stage, except for the zero-stage, is identical to every otherstage. The zero-stage is an input stage for the joint process estimator60. The first stage through the m^(th) -stage work on the signalproduced in the immediately previous stage, i.e., the (m-1)^(th) -stage,such that a good approximation to either the primary signal s".sub.λa(t) or the secondary signal n".sub.λa (t) is produced as output from them^(th) -stage.

The least-squares lattice predictor 70 comprises registers 90 and 92,summing elements 100 and 102, and delay elements 110. The registers 90and 92 contain multiplicative values of a forward reflection coefficientΓ_(f),m (t) and a backward reflection coefficient Γ_(b),m (t) whichmultiply the reference signal n'(t) or s'(t) and signals derived fromthe reference signal n'(t) or s'(t). Each stage of the least-squareslattice predictor outputs a forward prediction error f_(m) (t) and abackward prediction error b_(m) (t). The subscript m is indicative ofthe stage.

For each set of samples, i.e. one sample of the reference signal n'(t)or s'(t) derived substantially simultaneously with one sample of themeasured signal S.sub.λa (t), the sample of the reference signal n'(t)or s'(t) is input to the least-squares lattice predictor 70. Thezero-stage forward prediction error f₀ (t) and the zero-stage backwardprediction error b₀ (t) are set equal to the reference signal n'(t) ors'(t). The backward prediction error b₀ (t) is delayed by one sampleperiod by the delay element 110 in the first stage of the least-squareslattice predictor 70. Thus, the immediately previous value of thereference n'(t) or s'(t) is used in calculations involving thefirst-stage delay element 110. The zero-stage forward prediction erroris added to the negative of the delayed zero-stage backward predictionerror b₀ (t-1) multiplied by the forward reflection coefficient valueΓ_(f),1 (t) register 90 value, to produce a first-stage forwardprediction error f₁ (t). Additionally, the zero-stage forward predictionerror f₀ (t) is multiplied by the backward reflection coefficient valueΓ_(b),1 (t) register 92 value and added to the delayed zero-stagebackward prediction error b₀ (t-1) to produce a first-stage backwardprediction error b₁ (t). In each subsequent stage, m, of the leastsquare lattice predictor 70, the previous forward and backwardprediction error values, f_(m-1) (t) and b_(m-1) (t-1), the backwardprediction error being delayed by one sample period, are used to producevalues of the forward and backward prediction errors for the presentstage, f_(m) (t) and b_(m) (t).

The backward prediction error b_(m) (t) is fed to the concurrent stage,m, of the regression filter 80. There it is input to a register 96,which contains a multiplicative regression coefficient value κ_(m),λa(t). For example, in the zero-stage of the regression filter 80, thezero-stage backward prediction error b₀ (t) is multiplied by thezero-stage regression coefficient κ₀,λa (t) register 96 value andsubtracted from the measured value of the signal S.sub.λa (t) at asumming element 106 to produce a first stage estimation error signale₁,λa (t). The first-stage estimation error signal e₁,λa (t) is a firstapproximation to either the primary signal or the secondary signal. Thisfirst-stage estimation error signal e₁,λa (t) is input to thefirst-stage of the regression filter 80. The first-stage backwardprediction error b₁ (t), multiplied by the first-stage regressioncoefficient κ₁,λa (t) register 96 value is subtracted from thefirst-stage estimation error signal e₁,λa (t) to produce thesecond-stage estimation error e₂,λa (t). The second-stage estimationerror signal e₂,λa (t) is a second, somewhat better approximation toeither the primary signal s.sub.λa (t) or the secondary signal n.sub.λa(t).

The same processes are repeated in the least-squares lattice predictor70 and the regression filter 80 for each stage until a goodapproximation e_(m),λa (t), to either the primary signal s.sub.λa (t) orthe secondary signal n.sub.λa (t) is determined. Each of the signalsdiscussed above, including the forward prediction error f_(m) (t), thebackward prediction error b_(m) (t), the estimation error signale_(m),λa (t), is necessary to calculate the forward reflectioncoefficient Γ_(f),m (t), the backward reflection coefficient Γ_(b),m(t), and the regression coefficient ε_(m),λa (t) register 90, 92, and 96values in each stage, m. In addition to the forward prediction errorf_(m) (t), the backward prediction error b_(m) (t), and the estimationerror e_(m),λa (t) signals, a number of intermediate variables, notshown in FIG. 8 but based on the values labeled in FIG. 8, are requiredto calculate the forward reflection coefficient Γ_(f),m (t), thebackward reflection coefficient Γ_(b),m (t), and the regressioncoefficient κ_(m),λa (t) register 90, 92, and 96 values.

Intermediate variables include a weighted sum of the forward predictionerror squares _(m) (t), a weighted sum of the backward prediction errorsquares β_(m) (t), a scalar parameter Δ_(m) (t), a conversion factorγ_(m) (t), and another scalar parameter ρ_(m),λa (t). The weighted sumof the forward prediction errors _(m) (t) is defined as: ##EQU9## whereλ without a wavelength identifier, a or b, is a constant multiplicativevalue unrelated to wavelength and is typically less than or equal toone, i.e., λ≦1. The weighted sum of the backward prediction errors β_(m)(t) is defined as: ##EQU10## where, again, λ without a wavelengthidentifier, a or b, is a constant multiplicative value unrelated towavelength and is typically less than or equal to one, i.e., λ≦1. Theseweighted sum intermediate error signals can be manipulated such thatthey are more easily solved for, as described in Chapter 9, §9.3. anddefined hereinafter in equations (65) and (66).

DESCRIPTION OF THE JOINT PROCESS ESTIMATOR

The operation of the joint process estimator 60 is as follows. When thejoint process estimator 60 is turned on, the initial values ofintermediate variables and signals including the parameter Δ_(m-1) (t),the weighted sum of the forward prediction error signals _(m-1) (t), theweighted sum of the backward prediction error signals β_(m-1) (t), theparameter ρ_(m),λa (t), and the zero-stage estimation error e₀,λa (t)are initialized, some to zero and some to a small positive number δ:

    Δ.sub.m-1 (0)=0;                                     (52)

    .sub.m-1 (0)=δ;                                     (53)

    β.sub.m-1 (0)=δ;                                (54)

    ρ.sub.m,λa (0)=0;                               (55)

    e.sub.0,λa (t)=S.sub.λa (t) for t≧0.  (56)

After initialization, a simultaneous sample of the measured signalS.sub.λa (t) or S.sub.λb (t) and either the secondary reference n'(t) orthe primary reference s'(t) are input to the joint process estimator 60,as shown in FIG. 8. The forward and backward prediction error signals f₀(t) and b₀ (t), and intermediate variables including the weighted sumsof the forward and backward error signals ₀ (t) and β₀ (t), and theconversion factor γ₀ (t) are calculated for the zero-stage according to:

    f.sub.0 (t)=b.sub.0 (t)=n'(t)                              (57a)

    .sub.0 (t)=β.sub.0 (t)=λ .sub.0 (t-1)+|n'(t)|.sup.2                     (58a)

    γ.sub.0 (t-1)=1                                      (59a)

if a secondary reference n'(t) is used or according to:

    f.sub.0 (t)=b.sub.0 (t)=s'(t)                              (57b)

    .sub.0 (t)=β.sub.0 (t)=λ .sub.0 (t-1)+|s'(t)|.sup.2                     (58b)

    γ.sub.0 (t-1)=1                                      (59b)

if a primary reference s'(t) is used where, again, λ without awavelength identifier, a or b, is a constant multiplicative valueunrelated to wavelength.

Forward reflection coefficient Γ_(f),m (t), backward reflectioncoefficient Γ_(b),m (t), and regression coefficient κ_(m),λa (t)register 90, 92 and 96 values in each stage thereafter are set accordingto the output of the previous stage. The forward reflection coefficientΓ_(f),1 (t), backward reflection coefficient Γ_(b),1 (t), and regressioncoefficient κ₁,λa (t) register 90, 92 and 96 values in the first stageare thus set according to algorithm using values in the zero-stage ofthe joint process estimator 60. In each stage, m≧1, intermediate valuesand register values including the parameter Δ_(m-1) (t); the forwardreflection coefficient Γ_(f),m (t) register 90 value; the backwardreflection coefficient Γ_(b),m (t) register 92 value; the forward andbackward error signals f_(m) (t) and b_(m) (t); the weighted sum ofsquared forward prediction errors _(f),m (t), as manipulated in §9.3 ofthe Haykin book; the weighted sum of squared backward prediction errorsβ_(b),m (t), as manipulated in §9.3 of the Haykin book; the conversionfactor γ_(m) (t); the parameter ρ_(m),λa (t); the regression coefficientκ_(m),λa (t) register 96 value; and the estimation error e_(m+1)λa (t)value are set according to:

    Δ.sub.m-1 (t)=λΔ.sub.m-1 (t-1)+{b.sub.m-1 (t-1)f.sup.*.sub.m-1 (t)/γ.sub.m-1 (t-1)}           (60)

    Γ.sub.f,m (t)=-{Δ.sub.m-1 (t)/β.sub.m-1 (t-1)}(61)

    Γ.sub.b,m (t)=-{Δ.sup.*.sub.m-1 (t)/ .sub.m-1 (t)}(62)

    f.sub.m (t)=f.sub.m-1 (t)+Γ.sup.*.sub.f,m (t)b.sub.m-1 (t-1) (63)

    b.sub.m (t)=b.sub.m-1 (t-1)+Γ.sup.*.sub.b,m (t)f.sub.m-1 (t) (64)

    .sub.m (t)= .sub.m-1 (t)-{|Δ.sub.m-1 (t)|.sup.2 /βm-1(t-1)}                                          (65)

    β.sub.m (t)=β.sub.m-1 (t-1)-{|Δ.sub.m-1 (t)|.sup.2 / .sub.m-1 (t)}                       (66)

    γ.sub.m (t-1)=γ.sub.m-1 (t-1)-{|b.sub.m-1 (t-1)|.sup.2 /β.sub.m-1 (t-1)}              (67)

    ρ.sub.m,λa (t)=λ.sub.ρm,λa (t-1)+{b.sub.m (t)e.sup.*.sub.m,λa (t)/γ.sub.m (t)}         (68)

    κ.sub.m,λa (t)={ρ.sub.m,λa (t)/β.sub.m (t)}(69)

    e.sub.m+1,λa (t)=e.sub.m,λa (t)-κ.sup.*.sub.m (t)b.sub.m (t)                                            (70)

where a (^(*)) denotes a complex conjugate.

These equations cause the error signals f_(m) (t), b_(m) (t), e_(m),λa(t) to be squared or to be multiplied by one another, in effect squaringthe errors, and creating new intermediate error values, such as Δ_(m-1)(t). The error signals and the intermediate error values are recursivelytied together, as shown in the above equations (60) through (70). Theyinteract to minimize the error signals in the next stage.

After a good approximation to either the primary signal s.sub.λa (t) orthe secondary signal n.sub.λa (t) has been determined by the jointprocess estimator 60, a next set of samples, including a sample of themeasured signal S.sub.λa (t) and a sample of either the secondaryreference n'(t) or the primary reference s'(t), are input to the jointprocess estimator 60. The re-initialization process does not re-occur,such that the forward and backward reflection coefficient Γ_(f),m (t)and Γ_(b),m (t) register 90, 92 values and the regression coefficientκ_(m),λa (t) register 96 value reflect the multiplicative valuesrequired to estimate either the primary signal portion s.sub.λa (t) orthe secondary signal portion n.sub.λa (t) of the sample of S.sub.λa (t)input previously. Thus, information from previous samples is used toestimate either the primary or secondary signal portion of a present setof samples in each stage.

FLOWCHART OF JOINT PROCESS ESTIMATOR

In a signal processor, such as a physiological monitor, incorporating areference processor of the present invention to determine a referencen'(t) or s'(t) for input to a correlation canceler, a joint processestimator 60 type adaptive noise canceler is generally implemented via asoftware program having an iterative loop. One iteration of the loop isanalogous to a single stage of the joint process estimator as shown inFIG. 8. Thus, if a loop is iterated m times, it is equivalent to an mstage joint process estimator 60.

A flow chart of a subroutine to estimate the primary signal portions.sub.λa (t) or the secondary signal portion n.sub.λa (t) of a measuredsignal, S.sub.λa (t) is shown in FIG. 9. The flow chart describes howthe action of a reference processor for determining either the secondaryreference or the primary reference and the joint process estimator 60would be implemented in software.

A one-time only initialization is performed when the physiologicalmonitor is turned on, as indicated by an "INITIALIZE NOISE CANCELER" box120. The initialization sets all registers 90, 92, and 96 and delayelement variables 110 to the values described above in equations (52)through (56).

Next, a set of simultaneous samples of the measured signals S.sub.λa (t)and S.sub.λb (t) is input to the subroutine represented by the flowchartin FIG. 9. Then a time update of each of the delay element programvariables occurs, as indicated in a "TIME UPDATE OF [Z⁻¹ ] ELEMENTS" box130, wherein the value stored in each of the delay element variables 110is set to the value at the input of the delay element variable 110.Thus, the zero-stage backward prediction error b₀ (t) is stored in thefirst-stage delay element variable, the first-stage backward predictionerror b₁ (t) is stored in the second-stage delay element variable, andso on.

Then, using the set of measured signal samples S.sub.λa (t) and S.sub.λb(t), the reference signal is calculated according to the ratiometric orthe constant saturation method described above. This is indicated by a"CALCULATE REFERENCE [n'(t) or s'(t)] FOR TWO MEASURED SIGNAL SAMPLES"box 140.

A zero-stage order update is performed next as indicated in a"ZERO-STAGE UPDATE" box 150. The zero-stage backward prediction error b₀(t), and the zero-stage forward prediction error f₀ (t) are set equal tothe value of the reference signal n'(t) or s'(t). Additionally, theweighted sum of the forward prediction errors _(m) (t) and the weightedsum of backward prediction errors β_(m) (t) are set equal to the valuedefined in equations (53) and (54).

Next, a loop counter, m, is initialized as indicated in a "m=0" box 160.A maximum value of m, defining the total number of stages to be used bythe subroutine corresponding to the flowchart in FIG. 9, is alsodefined. Typically, the loop is constructed such that it stops iteratingonce a criterion for convergence upon a best approximation to either theprimary signal or the secondary signal has been met by the joint processestimator 60. Additionally, a maximum number of loop iterations may bechosen at which the loop stops iteration. In a preferred embodiment of aphysiological monitor of the present invention, a maximum number ofiterations, m=6 to m=10, is advantageously chosen.

Within the loop, the forward and backward reflection coefficient Γ_(f),m(t) and Γ_(b),m (t) register 90 and 92 values in the least-squareslattice filter are calculated first, as indicated by the "ORDER UPDATEMTH CELL OF LSL-LATTICE" box 170 in FIG. 9. This requires calculation ofintermediate variable and signal values used in determining register 90,92, and 96 values in the present stage, the next stage, and in theregression filter 80.

The calculation of regression filter register 96 value κ_(m),λa (t) isperformed next, indicated by the "ORDER UPDATE MTH STAGE OF REGRESSIONFILTER(S)" box 180. The two order update boxes 170 and 180 are performedin sequence m times, until m has reached its predetermined maximum (inthe preferred embodiment, m=6 to m=10) or a solution has been convergedupon, as indicated by a YES path from a "DONE" decision box 190. In acomputer subroutine, convergence is determined by checking if theweighted sums of the forward and backward prediction errors _(m) (t) andβ_(m) (t) are less than a small positive number. An output is calculatednext, as indicated by a "CALCULATE OUTPUT" box 200. The output is a goodapproximation to either the primary signal or secondary signal, asdetermined by the reference processor 26 and joint process estimator 60subroutine corresponding to the flow chart of FIG. 9. This is displayed(or used in a calculation in another subroutine), as indicated by a "TODISPLAY" box 210.

A new set of samples of the two measured signals S.sub.λa (t) andS.sub.λb (t) is input to the processor and joint process estimator 60adaptive noise canceler subroutine corresponding to the flowchart ofFIG. 9 and the process reiterates for these samples. Note, however, thatthe initialization process does not re-occur. New sets of measuredsignal samples S.sub.λa (t) and S.sub.λb (t) are continuously input tothe reference processor 26 and joint process estimator 60 adaptive noisecanceler subroutine. The output forms a chain of samples which isrepresentative of a continuous wave. This waveform is a goodapproximation to either the primary signal waveform s.sub.λa (t) or thesecondary waveform n.sub.λa (t) at wavelength λa. The waveform may alsobe a good approximation to either the primary signal waveform s.sub.λb(t) or the secondary waveform n".sub.λb (t) at wavelength λb.

CALCULATION OF SATURATION FROM CORRELATION CANCELER OUTPUT

Physiological monitors may use the approximation of the primary signalss".sub.λa (t) or s".sub.λb (t) or the secondary signals n".sub.λa (t) orn".sub.λb (t) to calculate another quantity, such as the saturation ofone constituent in a volume containing that constituent plus one or moreother constituents. Generally, such calculations require informationabout either a primary or secondary signal at two wavelengths. Forexample, the constant saturation method requires a good approximation ofthe primary signal portions s.sub.λa (t) and s.sub.λb (t) of bothmeasured signals S.sub.λa (t) and S.sub.λb (t). Then, the arterialsaturation is determined from the approximations to both signals, i.e.s".sub.λa (t) and s"_(b) (t). The constant saturation method alsorequires a good approximation of the secondary signal portions n.sub.λa(t) or n.sub.λb (t). Then an estimate of the venous saturation may bedetermined from the approximations to these signals i.e. n".sub.λa (t)and n".sub.λb (t).

In other physiological measurements, information about a signal at athird wavelength is necessary. For example, to find the saturation usingthe ratiometric method, signals S.sub.λa (t) and S.sub.λb (t) are usedto find the reference signal n'(t) or s'(t). But as discussedpreviously, λa and λb were chosen to satisfy a proportionalityrelationship like that of equation (22). This proportionalityrelationship forces the two primary signal portions s.sub.λa (t) ands.sub.λb (t) of equations (23c) and (24c) to be linearly dependent.Generally, linearly dependent mathematical equations cannot be solvedfor the unknowns. Analogously, some desirable information cannot bederived from two linearly dependent signals. Thus, to determine thesaturation using the ratiometric method, a third signal issimultaneously measured at wavelength λc. The wavelength λc is chosensuch that the primary portion s.sub.λc (t) of the measured signalS.sub.λc (t) is not linearly dependent with the primary portionss.sub.λa (t) and s.sub.λb (t) of the measured signals S.sub.λa (t) andS.sub.λb (t). Since all measurements are taken substantiallysimultaneously, the secondary reference signal n'(t) is correlated tothe secondary signal portions n.sub.λa, n.sub.λb, and n.sub.λc of eachof the measured signals S.sub.λa (t), S.sub.λb (t), and S.sub.λc (t) andcan be used to estimate approximations to the primary signal portionss.sub.λa (t), s.sub.λb (t), and s.sub.λc (t) for all three measuredsignals S.sub.λa (t), S.sub.λb (t), and S.sub.λc (t). Using theratiometric method, estimation of the ratio of signal portions s.sub.λa(t) and s.sub.λc (t) of the two measured signals S.sub.λa (t) andS.sub.λc (t), chosen correctly, is usually satisfactory to determinemost physiological data.

A joint process estimator 60 having two regression filters 80a and 80bis shown in FIG. 10. A first regression filter 80a accepts a measuredsignal S.sub.λa (t). A second regression filter 80b accepts a measuredsignal S.sub.λb (t) or S.sub.λc (t), depending whether the constantsaturation method or the ratiometric method is used to determine thereference signal n'(t) or s'(t) for the constant saturation method orn'(t) or s'(t) for the ratiometric method. The first and secondregression filters 80a and 80b are independent. The backward predictionerror b_(m) (t) is input to each regression filter 80a and 80b, theinput for the second regression filter 80b bypassing the firstregression filter 80a.

The second regression filter 80b comprises registers 98, and summingelements 108 arranged similarly to those in the first regression filter80a. The second regression filter 80b operates via an additionalintermediate variable in conjunction with those defined by equations(60) through (70), i.e.:

    ρ.sub.m,λb (t)=λ.sub.ρm,λb (t-1)+{b.sub.m (t)e.sup.*.sub.m,λb (t)/γ.sub.m (t)}; or     (71)

    ρ.sub.m,λc (t)=λ.sub.ρm,λc (t-1)+{b.sub.m (t)e.sup.*.sub.m,λc (t)/γ.sub.m (t)}; and    (72)

    ρ.sub.0,λb (0)=0; or                            (73)

    ρ.sub.0,λc (0)=0.                               (74)

The second regression filter 80b has an error signal value definedsimilar to the first regression filter error signal values, e_(m+1),λa(t), i.e.:

    e.sub.m+1,λb (t)=e.sub.m,λb (t)-κ.sup.*.sub.m,λb (t)b.sub.m (t); or                                        (75)

    e.sub.m+1,λc (t)=e.sub.m,λc (t)-κ.sup.*.sub.m,λb (t)b.sub.m (t); and                                       (76)

    e.sub.0,λb (t)=S.sub.λb (t) for t≧0; or (77)

    e.sub.0,λc (t)=S.sub.λc (t) for t≧0.  (78)

The second regression filter has a regression coefficient κ_(m),λb (t)register 98 value defined similarly to the first regression filter errorsignal values, i.e.:

    κ.sub.m,λb (t)={ρ.sub.m,λb (t)/β.sub.m (t)}; or                                                        (79)

    κ.sub.m,λc (t)={ρ.sub.m,λc (t)/β.sub.m (t)}; (80)

These values are used in conjunction with those intermediate variablevalues, signal values, register and register values defined in equations(52) through (70). These signals are calculated in an order defined byplacing the additional signals immediately adjacent a similar signal forthe wavelength λa.

For the ratiometric method, S.sub.λc (t) is input to the secondregression filter 80b. The output of the second regression filter 80b isthen a good approximation to the primary signal s".sub.λc (t) orsecondary signal n".sub.λc (t). For the constant saturation method,S.sub.λb (t) is input to the second regression filter 80b. The output isthen a good approximation to the primary signal s".sub.λb (t) orsecondary signal s".sub.λb (t).

The addition of the second regression filter 80b does not substantiallychange the computer program subroutine represented by the flowchart ofFIG. 9. Instead of an order update of the m^(th) stage of only oneregression filter, an order update of the m^(th) stage of bothregression filters 80a and 80b is performed. This is characterized bythe plural designation in the "ORDER UPDATE OF m^(th) STAGE OFREGRESSION FILTER(S)" box 180 in FIG. 9. Since the regression filters80a and 80b operate independently, independent calculations can beperformed in the reference processor and joint process estimator 60adaptive noise canceler subroutine modeled by the flowchart of FIG. 9.

CALCULATION OF SATURATION

Once good approximations to the primary signal portions, s".sub.λa (t)and s".sub.λc (t) or the secondary signal portions n".sub.λa (t) andn".sub.λc (t) for the ratiometric method and s".sub.λa (t) and s".sub.λb(t) or n".sub.λa (t) and n".sub.λc (t) for the constant saturationmethod, have been determined by the joint process estimator 60, thesaturation of A₅ in a volume containing A₅ and A₆, for example, may becalculated according to various known methods. Mathematically, theapproximations to the primary signals can be written:

    s".sub.λa (t)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t) +ε.sub.5,λa c.sub.3 x.sub.3,4 (t)+ε.sub.6,λa c.sub.4 x.sub.3,4 (t)                                     (81a)

    s".sub.λc (t)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t) +ε.sub.5,λc c.sub.3 x.sub.3,4 (t)+ε.sub.6,λc c.sub.4 x.sub.3,4 (t)                                     (82a)

for the ratiometric method using wavelengths λa and λc, and assumingthat the secondary reference n'(t) is uncorrelated with x₃,4 (t) andx₅,6 (t). Terms involving x₃,4 (t) and x₅,6 (t) may then be separatedusing the constant saturation method. It is important to understand thatif n'(t) is uncorrelated with x₃,4 (t) and x₅,6 (t), use of theratiometric method followed by use of the constant saturation methodresults in a more accurate computation of the saturation of A₃ in thelayer x₃,4 then by use of the ratiometric or constant saturation methodsalone. In the event that n'(t) and x₃,4 (t) are correlated theratiometric method yields

    s".sub.λa (t)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t); and  (81b)

    s".sub.λc (t)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t).      (82b)

For the constant saturation method, the approximations to the primarysignals can be written, in terms of λa and λb, as:

    s".sub.λa (t)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t); and  (83)

    s".sub.λb (t)≈ε.sub.5,λb c.sub.5 x.sub.5,6 (t)+ε.sub.6,λb c.sub.6 x.sub.5,6 (t).      (84)

Equations (81b), (82b), (83) and (84) are equivalent to two equationshaving three unknowns, namely c₅ (t), c₆ (t) and x₅,6 (t). In both theratiometric and the constant saturation cases, the saturation can bedetermined by acquiring approximations to the primary or secondarysignal portions at two different, yet proximate times t₁ and t₂ overwhich the saturation of A₅ in the volume containing A₅ and A₆ and thesaturation of A₃ in the volume containing A₃ and A₄ does not changesubstantially. For example, for the primary signals estimated by theratiometric method, at times t₁ and t₂ :

    s".sub.λa (t.sub.1)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t.sub.1)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t.sub.1) (85)

    s".sub.λc (t.sub.1)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t.sub.1)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t.sub.1) (86)

    s".sub.λa (t.sub.2)≈ε.sub.5,λa c.sub.5 x.sub.5,6 (t.sub.2)+ε.sub.6,λa c.sub.6 x.sub.5,6 (t.sub.2) (87)

    s".sub.λc (t.sub.2)≈ε.sub.5,λc c.sub.5 x.sub.5,6 (t.sub.2)+ε.sub.6,λc c.sub.6 x.sub.5,6 (t.sub.2) (88)

Then, difference signals may be determined which relate the signals ofequations (85) through (88), i.e.:

    Δs.sub.λa =s".sub.λa (t.sub.1)-s".sub.λa (t.sub.2)≈ε.sub.5,λa c.sub.5 Δx+ε.sub.6,λa c.sub.6 Δx; and  (89)

    Δs.sub.λc =s".sub.λc (t.sub.1)-s".sub.λc (t.sub.2)≈ε.sub.5,λc c.sub.5 Δx+ε.sub.,λc c.sub.6 Δx;       (90)

where Δx=x₅,6 (t₁)-x₅,6 (t₂). The average saturation at time t=(t₁+t₂)/2 is:

    Saturation(t)=c.sub.5 (t)/[c.sub.5 (t)+c.sub.6 (t)]        (91) ##EQU11## It will be understood that the Δx term drops out from the saturation calculation because of the division. Thus, knowledge of the thickness of the primary constituents is not required to calculate saturation.

PULSE OXIMETRY MEASUREMENTS

A specific example of a physiological monitor utilizing a processor ofthe present invention to determine a secondary reference n'(t) for inputto a correlation canceler that removes erratic motion-induced secondarysignal portions is a pulse oximeter. Pulse oximetry may also beperformed utilizing a processor of the present invention to determine aprimary signal reference s'(t) which may be used for display purposes orfor input to a correlation canceler to derive information about patientmovement and venous blood oxygen saturation.

A pulse oximeter typically causes energy to propagate through a mediumwhere blood flows close to the surface for example, an ear lobe, or adigit such as a finger, or a forehead. An attenuated signal is measuredafter propagation through or reflected from the medium. The pulseoximeter estimates the saturation of oxygenated blood.

Freshly oxygenated blood is pumped at high pressure from the heart intothe arteries for use by the body. The volume of blood in the arteriesvaries with the heartbeat, giving rise to a variation in absorption ofenergy at the rate of the heartbeat, or the pulse.

Oxygen depleted, or deoxygenated, blood is returned to the heart by theveins along with unused oxygenated blood. The volume of blood in theveins varies with the rate of breathing, which is typically much slowerthan the heartbeat. Thus, when there is no motion induced variation inthe thickness of the veins, venous blood causes a low frequencyvariation in absorption of energy. When there is motion inducedvariation in the thickness of the veins, the low frequency variation inabsorption is coupled with the erratic variation in absorption due tomotion artifact.

In absorption measurements using the transmission of energy through amedium, two light emitting diodes (LED's) are positioned on one side ofa portion of the body where blood flows close to the surface, such as afinger, and a photodetector is positioned on the opposite side of thefinger. Typically, in pulse oximetry measurements, one LED emits avisible wavelength, preferably red, and the other LED emits an infraredwavelength. However, one skilled in the art will realize that otherwavelength combinations could be used.

The finger comprises skin, tissue, muscle, both arterial blood andvenous blood, fat, etc., each of which absorbs light energy differentlydue to different absorption coefficients, different concentrations, anddifferent thicknesses. When the patient is not moving, absorption issubstantially constant except for the flow of blood. The constantattenuation can be determined and subtracted from the signal viatraditional filtering techniques. When the patient moves, the absorptionbecomes erratic. Erratic motion induced noise typically cannot bepredetermined and/or subtracted from the measured signal via traditionalfiltering techniques. Thus, determining the oxygen saturation ofarterial blood and venous blood becomes more difficult.

A schematic of a physiological monitor for pulse oximetry is shown inFIG. 11. Two LED's 300 and 302, one LED 300 emitting red wavelengths andanother LED 302 emitting infrared wavelengths, are placed adjacent afinger 310. A photodetector 320, which produces an electrical signalcorresponding to the attenuated visible and infrared light energysignals is located opposite the LED's 300 and 302. The photodetector 320is connected to a single channel of common processing circuitryincluding an amplifier 330 which is in turn connected to a band passfilter 340. The band pass filter 340 passes it output signal into asynchronized demodulator 350 which has a plurality of output channels.One output channel is for signals corresponding to visible wavelengthsand another output channel is for signals corresponding to infraredwavelengths.

The output channels of the synchronized demodulator for signalscorresponding to both the visible and infrared wavelengths are eachconnected to separate paths, each path comprising further processingcircuitry. Each path includes a DC offset removal element 360 and 362,such as a differential amplifier, a programmable gain amplifier 370 and372 and a low pass filter 380 and 382. The output of each low passfilter 380 and 382 is amplified in a second programmable gain amplifier390 and 392 and then input to a multiplexer 400.

The multiplexer 400 is connected to an analog-to-digital converter 410which is in turn connected to a microprocessor 420. Control linesbetween the microprocessor 420 and the multiplexer 400, themicroprocessor 420 and the analog-to-digital converter 410, and themicroprocessor 420 and each programmable gain amplifier 370, 372, 390,and 392 are formed. The microprocessor 420 has additional control lines,one of which leads to a display 430 and the other of which leads to anLED driver 440 situated in a feedback loop with the two LED's 300 and302.

The LED's 300 and 302 each emits energy which is absorbed by the finger310 and received by the photodetector 320. The photodetector 320produces an electrical signal which corresponds to the intensity of thelight energy striking the photodetector 320 surface. The amplifier 330amplifies this electrical signal for ease of processing. The band passfilter 340 then removes unwanted high and low frequencies. Thesynchronized demodulator 350 separates the electrical signal intoelectrical signals corresponding to the red and infrared light energycomponents. A predetermined reference voltage, V_(ref), is subtracted bythe DC offset removal element 360 and 362 from each of the separatesignals to remove substantially constant absorption which corresponds toabsorption when there is no motion induced signal component. Then thefirst programmable gain amplifiers 370 and 372 amplify each signal forease of manipulation. The low pass filters 380 and 382 integrate eachsignal to remove unwanted high frequency components and the secondprogrammable gain amplifiers 390 and 392 amplify each signal for furtherease of processing.

The multiplexer 400 acts as an analog switch between the electricalsignals corresponding to the red and the infrared light energy, allowingfirst a signal corresponding to the red light to enter theanalog-to-digital converter 410 and then a signal corresponding to theinfrared light to enter the analog-to-digital converter 410. Thiseliminates the need for multiple analog-to-digital converters 410. Theanalog-to-digital converter 410 inputs the data into the microprocessor420 for calculation of either a primary or secondary reference signalvia the processing technique of the present invention and removal orderivation of motion induced signal portions via a correlation canceler,such as an adaptive noise canceler. The microprocessor 420 centrallycontrols the multiplexer 400, the analog-to-digital converter 410, andthe first and second programmable gain amplifiers 370 and 390 for boththe red and the infrared channels. Additionally, the microprocessor 420controls the intensity of the LED's 302 and 304 through the LED driver440 in a servo loop to keep the average intensity received at thephotodetector 320 within an appropriate range. Within the microprocessor420 a reference signal n'(t) or s'(t) is calculated via either theconstant saturation method or the ratiometric method, as describedabove, the constant saturation method being generally preferred. Thissignal is used in an adaptive noise canceler of the joint processestimator type 60, as described above.

The multiplexer 400 time multiplexes, or sequentially switches between,the electrical signals corresponding to the red and the infrared lightenergy. This allows a single channel to be used to detect and beginprocessing the electrical signals. For example, the red LED 300 isenergized first and the attenuated signal is measured at thephotodetector 320. An electrical signal corresponding to the intensityof the attenuated red light energy is passed to the common processingcircuitry. The infrared LED 302 is energized next and the attenuatedsignal is measured at the photodetector 320. An electrical signalcorresponding to the intensity of the attenuated infrared light energyis passed to the common processing circuitry. Then, the red LED 300 isenergized again and the corresponding electrical signal is passed to thecommon processing circuitry. The sequential energization of LED's 300and 302 occurs continuously while the pulse oximeter is operating.

The processing circuitry is divided into distinct paths after thesynchronized demodulator 350 to ease time constraints generated by timemultiplexing. In the preferred embodiment of the pulse oximeter shown inFIG. 11, a sample rate, or LED energization rate, of 625 Hz isadvantageously employed. Thus, electrical signals reach the synchronizeddemodulator 350 at a rate of 625 Hz. Time multiplexing is not used inplace of the separate paths due to settling time constraints of the lowpass filters 380, 382, and 384.

In FIG. 11, a third LED 304 is shown adjacent the finger, located nearthe LED's 300 and 302. The third LED 304 is used to measure a thirdsignal S.sub.λc (t) to be used to determine saturation using theratiometric method. The third LED 304 is time multiplexed with the redand infrared LED's 300 and 302. Thus, a third signal is input to thecommon processing circuitry in sequence with the signals from the redand infrared LED's 300 and 302. After passing through and beingprocessed by the operational amplifier 330, the band pass filter 340,and the synchronized demodulator 350, the third electrical signalcorresponding to light energy at wavelength λc is input to a separatepath including a DC offset removal element 364, a first programmablegain amplifier 374, a low pass filter 384, and a second programmablegain amplifier 394. The third signal is then input to the multiplexer400.

The dashed line connection for the third LED 304 indicates that thisthird LED 304 is incorporated into the pulse oximeter when theratiometric method is used; it is unnecessary for the constantsaturation method. When the third LED 304 is used, the multiplexer 400acts as an analog switch between all three LED 300, 302, and 304signals. If the third LED 304 is utilized, feedback loops between themicroprocessor 420 and the first and second programmable gain amplifier374 and 394 in the λc wavelength path are also formed.

For pulse oximetry measurements using the ratiometric method, thesignals (logarithm converted) transmitted through the finger 310 at eachwavelength λa, λb, and λc are:

    S.sub.λa (t)=S.sub.λred1 (t)=ε.sub.HbO2,λa c.sup.A.sub.HbO2 x.sup.A (t)+ε.sub.Hb,λa c.sup.A.sub.Hb x.sup.A (t) +ε.sub.HbO2,λa c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λa c.sup.V.sub.Hb x.sup.V (t)+n.sub.λa (t).                                                      (93)

    S.sub.λb (t)=S.sub.λred2 (t)=ε.sub.HbO2,λb c.sup.A.sub.HbO2 x.sup.A (t)+ε.sub.Hb,λb c.sup.A.sub.Hb x.sup.A (t) +ε.sub.HbO2,λb c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λb c.sup.V.sub.Hb x.sup.V (t)+n.sub.λb (t).                                                      (94)

    S.sub.λc (t)=S.sub.λIR (t)=ε.sub.HbO2,λc c.sup.A.sub.HbO2 x.sup.A (t)+ε.sub.Hb,λc c.sup.A.sub.Hb x.sup.A (t) +ε.sub.HbO2,λc c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λc c.sup.V.sub.Hb x.sup.V (t)+n.sub.λc (t).                                                      (95)

In equations (93) through (95), x^(A) (t) is the lump-sum thickness ofthe arterial blood in the finger; x^(V) (t) is the lump-sum thickness ofvenous blood in the finger; ε_(HbO2),λa ε_(HbO2),λb, ε_(HbO2),λc,ε_(Hb),λa, ε_(Hb),λb, and ε_(Hb),λc are the absorption coefficients ofthe oxygenated and non-oxygenated hemoglobin, at each wavelengthmeasured; and c_(HbO2) (t) and c_(Hb) (t) with the superscriptdesignations A and V are the concentrations of the oxygenated andnon-oxygenated arterial blood and venous blood, respectively.

For the ratiometric method, the wavelengths chosen are typically two inthe visible red range, i.e., λa and λb, and one in the infrared range,i.e., λc. As described above, the measurement wavelengths λa and λb areadvantageously chosen to satisfy a proportionality relationship whichremoves the primary signal portions s.sub.λa (t) and s.sub.λb (t),yielding a secondary reference n'(t). In the preferred embodiment, theratiometric method is used to determine the secondary reference signaln'(t) by picking two wavelengths that cause the primary portionss.sub.λa (t) and s.sub.λb (t) of the measured signals S.sub.λa (t) andS.sub.λb (t) to become linearly dependent similarly to equation (22);i.e. wavelengths λa and λb which satisfy:

    ε.sub.HbO2,λa /ε.sub.Hb,λa =ε.sub.HbO2,λb /ε.sub.Hb,λb (96)

Typical wavelength values chosen are λa=650 nm and λb=685 nm.Additionally a typical wavelength value for λc is λc=940 nm. By pickingwavelengths λa and λb to satisfy equation (96) the venous portion of themeasured signal is also caused to become linearly dependent even thoughit is not usually considered to be part of the primary signals as is thecase in the constant saturation method. Thus, the venous portion of thesignal is removed with the primary portion of the constant saturationmethod. The proportionality relationship between equations (93) and (94)which allows determination of a non-zero secondary reference signaln'(t), similarly to equation (25) is:

    ω.sub.av =ε.sub.Hb,λa /ε.sub.Hb,λb ; where                                                     (97)

    n.sub.λa (t)≠ω.sub.av n.sub.λb (t). (98)

In pulse oximetry, both equations (97) and (98) can typically besatisfied simultaneously.

FIG. 12 is a graph of the absorption coefficients of oxygenated anddeoxygenated hemoglobin (ε_(HbO2) and ε_(Hb)) vs. wavelength (λ). FIG.13 is a graph of the ratio of the absorption coefficients vs.wavelength, i.e., ε_(Hb) /ε_(HbO2) vs. λ over the range of wavelengthwithin circle 13 in FIG. 12. Anywhere a horizontal line touches thecurve of FIG. 13 twice, as does line 400, the condition of equation (96)is satisfied. FIG. 14 shows an exploded view of the area of FIG. 12within the circle 13. Values of ε_(HbO2) and ε_(Hb) at the wavelengthswhere a horizontal line touches the curve of FIG. 13 twice can then bedetermined from the data in FIG. 14 to solve for the proportionalityrelationship of equation (97).

A special case of the ratiometric method is when the absorptioncoefficients ε_(HbO2) and ε_(Hb) are equal at a wavelength. Arrow 410 inFIG. 12 indicates one such location, called an isobestic point. FIG. 14shows an exploded view of the isobestic point. To use isobestic pointswith the ratiometric method, two wavelengths at isobestic points aredetermined to satisfy equation (96)

Multiplying equation (94) by ω_(av) and then subtracting equation (94)from equation (93), a non-zero secondary reference signal n'(t) isdetermined by:

    n'(t)=S.sub.λa (t)-ω.sub.av S.sub.λb (t)=n.sub.λa (t)-ω.sub.av n.sub.λb.   (99)

This secondary reference signal n'(t) has spectral content correspondingto the erratic, motion-induced noise. When it is input to a correlationcanceler, such as an adaptive noise canceler, with either the signalsS.sub.λa (t) and S.sub.λc (t) or S.sub.λb (t) and S.sub.λc (t) input totwo regression filters 80a and 80b as in FIG. 10, the adaptive noisecanceler will function much like an adaptive multiple notch filter andremove frequency components present in both the secondary referencesignal n'(t) and the measured signals from the measured signals S.sub.λa(t) and S.sub.λc (t) or S.sub.λb (t) and S.sub.λc (t). If the secondaryreference signal n'(t) is correlated to the venous portion, then theadaptive noise canceler is able to remove erratic noise caused in thevenous portion of the measured signals S.sub.λa (t), S.sub.λb (t), andS.sub.λc (t) even though the venous portion of the measured signalsS.sub.λa (t) and S.sub.λb (t) was not incorporated in the secondaryreference signal n'(t). In the event that the secondary reference signaln'(t) is not correlated to the venous component, then, the adaptivenoise canceler generally will not remove the venous portion from themeasured signals. However, a band pass filter applied to theapproximations to the primary signals s".sub.λa (t) and s".sub.λc (t) ors".sub.λb (t) and s".sub.λc (t) can remove the low frequency venoussignal due to breathing.

For pulse oximetry measurements using the constant saturation method,the signals (logarithm converted) transmitted through the finger 310 ateach wavelength λa and λb are:

    S.sub.λa (t)=S.sub.λred1 (t)=ε.sub.HbO2,λa c.sup.A.sub.HbO2 x.sup.A (t)+ε.sub.Hb,λa c.sup.A.sub.Hb x.sup.A (t) +ε.sub.HbO2,λa c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λa c.sup.V.sub.Hb x.sup.V (t)+n.sub.λa (t)                                                       (100a)

    S.sub.λa (t)=ε.sub.HbO2,λa c.sup.A.sub.HbO2 x.sup.A(t)+ε.sub.Hb,λa c.sup.A.sub.Hb x.sup.A (t)+n.sub.λa (t)                                                       (100b)

    S.sub.λa (t)=s.sub.λa (t)+n.sub.λa (t) (100c)

    S.sub.λb (t)=S.sub.λred2 (t)=ε.sub.HbO2,λb c.sup.A.sub.HbO2 x.sup.A (t)+ε.sub.Hb,λb c.sup.A.sub.Hb x.sup.A (t) +ε.sub.HbO2,λa c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λa c.sup.V.sub.Hb x.sup.V (t)+n.sub.λa (t)                                                       (101a)

    S.sub.λb (t)=ε.sub.HbO2,λb c.sup.A.sub.HbO2 x.sup.A(t)+ε.sub.Hb,λb c.sup.A.sub.Hb x.sup.A (t)+n.sub.λb (t)                                                       (101b)

    S.sub.λb (t)=s.sub.λb (t)+n.sub.λb (t) (101c)

For the constant saturation method, the wavelengths chosen are typicallyone in the visible red range, i.e., λa, and one in the infrared range,i.e., λb. Typical wavelength values chosen are λa=660 nm and λb=940 nm.Using the constant saturation method, it is assumed that c^(A) _(HbO2)(t)/c^(A) _(Hb) (t)=constant₁ and c^(V) _(HbO2) (t)/c^(V) _(Hb)(t)=constant₂. The oxygen saturation of arterial and venous bloodchanges slowly, if at all, with respect to the sample rate, making thisa valid assumption. The proportionality factors for equations (100) and(101) can then be written as: ##EQU12##

    s.sub.λa (t)=ω.sub.a (t)s.sub.λb (t)   (103a)

    n.sub.λa (t)≈ω.sub.a (t)n.sub.λb (t) (104a)

    n.sub.λa (t)=ω.sub.v (t)n.sub.λb (t)   (103b)

    s.sub.λa (t)≈ω.sub.v (t)s.sub.λb (t) (104b)

In pulse oximetry, it is typically the case that both equations (103)and (104) can be satisfied simultaneously.

Multiplying equation (101) by ω_(a) (t) and then subtracting equation(101) from equation (100), a non-zero secondary reference signal n'(t)is determined by:

    n'(t)=S.sub.λa (t)-ω.sub.a (t)S.sub.λb (t) (105a)

    =ε.sub.HbO2,λa c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λa c.sup.V.sub.Hb x.sup.V (t)+n.sub.λa (t) -ω.sub.a (t)[ε.sub.HbO2,λb c.sup.V.sub.HbO2 x.sup.V (t)+ε.sub.Hb,λb c.sup.V.sub.Hb x.sup.V (t)+n.sub.λb (t)].                                 (106a)

Multiplying equation (101) by ω_(v) (t) and then subtracting equation(101) from equation (100), a non-zero primary reference signal s'(t) isdetermined by:

    s'(t)=S.sub.λa (t)-ω.sub.v (t)S.sub.λb (t) (105b)

    =s.sub.λa (t)-ω.sub.v (t)s.sub.λb (t)  (106b)

The constant saturation assumption does not cause the venouscontribution to the absorption to be canceled along with the primarysignal portions s.sub.λa (t) and s.sub.λb (t), as did the relationshipof equation (96) used in the ratiometric method. Thus, frequenciesassociated with both the low frequency modulated absorption due tovenous absorption when the patient is still and the erraticallymodulated absorption due to venous absorption when the patient is movingare represented in the secondary reference signal n'(t). Thus, thecorrelation canceler can remove or derive both erratically modulatedabsorption due to venous blood in the finger under motion and theconstant low frequency cyclic absorption of venous blood.

Using either method, a primary reference s'(t) or a secondary referencen'(t) is determined by the processor of the present invention for use ina correlation canceler, such as an adaptive noise canceler, which isdefined by software in the microprocessor. The preferred adaptive noisecanceler is the joint process estimator 60 described above.

Illustrating the operation of the ratiometric method of the presentinvention, FIGS. 15, 16 and 17 show signals measured for use indetermining the saturation of oxygenated arterial blood using areference processor of the present invention which employs theratiometric method, i.e., the signals S.sub.λa (t)=S.sub.λred1 (t),S.sub.λb (t)=S.sub.λred2 (t), and S.sub.λc (t)=S.sub.λIR (t). A firstsegment 15a, 16a, and 17a of each of the signals is relativelyundisturbed by motion artifact, i.e., the patient did not movesubstantially during the time period in which these segments weremeasured. These segments 15a, 16a, and 17a are thus generallyrepresentative of the plethysmographic waveform at each of the measuredwavelengths. These waveforms are taken to be the primary signalss.sub.λa (t), s.sub.λb (t), and s.sub.λc (t). A second segment 15b, 16b,and 17b of each of the signals is affected by motion artifact, i.e., thepatient did move during the time period in which these segments weremeasured. Each of these segments 15b, 16b, and 17b shows large motioninduced excursions in the measured signal These waveforms contain bothprimary plethysmographic signals and secondary motion inducedexcursions. A third segment 15c, 16c, and 17c of each of the signals isagain relatively unaffected by motion artifact and is thus generallyrepresentative of the plethysmographic waveform at each of the measuredwavelengths.

FIG. 18 shows the secondary reference signal n'(t)=n.sub.λa -ω_(av)n.sub.λb (t), as determined by a reference processor of the presentinvention utilizing the ratiometric method. As discussed previously, thesecondary reference signal n'(t) is correlated to the secondary signalportions n.sub.λa, n.sub.λb, and n.sub.λc. Thus, a first segment 18a ofthe secondary reference signal n'(t) is generally flat, corresponding tothe fact that there is very little motion induced noise in the firstsegments 15a, 16a, and 17a of each signal. A second segment 18b of thesecondary reference signal n'(t) exhibits large excursions,corresponding to the large motion induced excursions in each of themeasured signals. A third segment 18c of the secondary reference signaln'(t) is generally flat, again corresponding to the lack of motionartifact in the third segments 15c, 16c, and 17c of each measuredsignal.

FIG. 19 shows the primary reference signal s'(t)=s.sub.λa -ω_(e)s.sub.λb (t), as determined by a reference processor of the presentinvention utilizing the ratiometric method. As discussed previously, theprimary reference signal s'(t) is correlated to the primary signalportions s.sub.λa (t), s.sub.λb (t), and s.sub.λc (t). Thus, a firstsegment 19a of the primary reference signal s'(t) is indicative of theplethysmographic waveform, corresponding to the fact that there is verylittle motion induced noise in the first segments 15a, 16a, and 17a ofeach signal. A second segment 19b of the primary reference signal s'(t)also exhibits a signal related to a plethymographic waveform,corresponding to each of the measured signals in the absence of thelarge motion induced excursions. A third segment 19c of the primaryreference signal s'(t) is generally indicative of the plethysmographicwaveform, again corresponding to the lack of motion artifact in thethird segments 15c, 16c, and 17c of each measured signal.

FIGS. 20 and 21 show the approximations s".sub.λa (t) and s".sub.λc (t)to the primary signals s.sub.λa (t) and s.sub.λc (t) as estimated by thecorrelation canceler 27 using a secondary reference signal n'(t)determined by the ratiometric method. FIGS. 20 and 21 illustrate theeffect of correlation cancelation using the secondary reference signaln'(t) as determined by the reference processor of the present inventionusing the ratiometric method. Segments 20b and 21b are not dominated bymotion induced noise as were segments 15b, 16b, and 17b of the measuredsignals. Additionally, segments 20a, 21a, 20c, and 21c have not beensubstantially changed from the measured signal segments 15a, 17a, 15c,and 17c where there was no motion induced noise.

FIGS. 22 and 23 show the approximations n".sub.λa (t) and n".sub.λc (t)to the primary signals n.sub.λa (t) and n.sub.λc (t) as estimated by thecorrelation canceler 27 using a primary reference signal s'(t)determined by the ratiometric method. Note that the scale of FIGS. 15through 23 is not the same for each figure to better illustrate changesin each signal. FIGS. 22 and 23 illustrate the effect of correlationcancelation-using the primary reference signal s'(t) as determined bythe reference processor of the present invention using the ratiometricmethod. Only segments 22b and 23b are dominated by motion induced noiseas were segments 15b, 16b, and 17b of the measured signals.Additionally, segments 22a, 23a, 22c, and 23c are nearly zerocorresponding to the measured signal segments 15a, 17a, 15c, and 17cwhere there was no motion induced noise.

Illustrating the operation of the constant saturation method of thepresent invention, FIGS. 24 and 25 show signals measured for input to areference processor of the present invention which employs the constantsaturation method, i.e., the signals S.sub.λa (t)=S.sub.λred (t) ands.sub.λb (t)=S.sub.λIR (t). A first segment 24a and 25a of each of thesignals is relatively undisturbed by motion artifact, i.e., the patientdid not move substantially during the time period in which thesesegments were measured. These segments 24a and 25a are thus generallyrepresentative of the primary plethysmographic waveform at each of themeasured wavelengths. A second segment 24b and 25b of each of thesignals is affected by motion artifact, i.e., the patient did moveduring the time period in which these segments were measured. Each ofthese segments 24b and 25b shows large motion induced excursions in themeasured signal. A third segment 24c and 25c of each of the signals isagain relatively unaffected by motion artifact and is thus generallyrepresentative of the primary plethysmographic waveform at each of themeasured wavelengths.

FIG. 26 shows the secondary reference signal n'(t)=n.sub.λa (t)-ω_(a)n.sub.λb (t), as determined by a reference processor of the presentinvention utilizing the constant saturation method. Again, the secondaryreference signal n'(t) is correlated to the secondary signal portionsnλa and n.sub.λb. Thus, a first segment 26a of the secondary referencesignal n'(t) is generally flat, corresponding to the fact that there isvery little motion induced noise in the first segments 24a and 25a ofeach signal. A second segment 26b of the secondary reference signaln'(t) exhibits large excursions, corresponding to the large motioninduced excursions in each of the measured signals. A third segment 26cof the noise reference signal n'(t) is generally flat, againcorresponding to the lack of motion artifact in the third segments 24cand 25c of each measured signal.

FIG. 27 shows the primary reference signal s'(t)=s.sub.λa -ω_(v)s.sub.λb (t), as determined by a reference processor of the presentinvention utilizing the constant saturation method. As discussedpreviously, the primary reference signal s'(t) is correlated to theprimary signal portions s.sub.λa (t) and s.sub.λb (t). Thus, a firstsegment 27a of the primary reference signal s'(t) is indicative of theplethysmographic waveform, corresponding to the fact that there is verylittle motion induced noise in the first segments 24a and 25a of eachsignal. A second segment 27b of the primary reference signal s'(t) alsoexhibits a signal related to a plethymographic waveform, correspondingto each of the measured signals in the absence of the large motioninduced excursions. A third segment 27c of the primary reference signals'(t) is generally indicative of the plethysmographic waveform, againcorresponding to the lack of motion artifact in the third segments 24cand 25c of each measured signal.

FIGS. 28 and 29 show the approximations s".sub.λa (t) and s".sub.λb (t)to the primary signals s.sub.λa (t) and s.sub.λb (t) as estimated by thecorrelation canceler 27 using a secondary reference signal n'(t)determined by the constant saturation method. FIGS. 28 and 29 illustratethe effect of correlation cancelation using the secondary referencesignal n'(t) as determined by a reference processor of the presentinvention utilizing the constant saturation method. Segments 28b and 28bare not dominated by motion induced noise as were segments 24b and 25bof the measured signals. Additionally, segments 28a, 29a, 28c, and 29chave not been substantially changed from the measured signal segments24a, 25a 24c and 25c where there was no motion induced noise.

FIGS. 30 and 31 show the approximations n".sub.λa (t) and n".sub.λb (t)to the secondary signals n.sub.λa (t) and n.sub.λb (t) as estimated bythe correlation canceler 27 using a primary reference signal s'(t)determined by the constant saturation method. Note that the scale ofFIGS. 24 through 31 is not the same for each figure to better illustratechanges in each signal. FIGS. 30 and 31 illustrate the effect ofcorrelation cancelation using the primary reference signal s'(t) asdetermined by a reference processor of the present invention utilizingthe constant saturation method. Only segments 30b and 31b are dominatedby motion induced noise as were segments 24b, and 25b of the measuredsignals. Additionally, segments 30a, 31a, 30c, and 31c are nearly zerocorresponding to the measured signal segments 24a, 25a, 24c, and 25cwhere there was no motion induced noise.

METHOD FOR ESTIMATING PRIMARY AND SECONDARY SIGNAL PORTIONS OF MEASUREDSIGNALS IN A PULSE OXIMETER

A copy of a computer subroutine, written in the C programming language,calculates a primary reference s'(t) and a secondary reference n'(t)using the ratiometric method and, using a joint process estimator 60,estimates either the primary or secondary signal portions of twomeasured signals, each having a primary signal which is correlated withthe primary reference s'(t) and having a secondary signal which iscorrelated with the secondary reference n'(t), is appended in AppendixA. For example, S.sub.λa (t)=S.sub.λred (t)=S.sub.λ660 nm (t) andS.sub.λb (t)=S.sub.λIR (t)=S.sub.λ940 nm (t) can be input to thecomputer subroutine. This subroutine is one way to implement the stepsillustrated in the flowchart of FIG. 9 for a monitor particularlyadapted for pulse oximetry.

The program estimates either the primary signal portions or thesecondary signal portions of two light energy signals, one preferablycorresponding to light in the visible red range and the other preferablycorresponding to light in the infrared range such that a determinationof the amount of oxygen, or the saturation of oxygen in the arterial andvenous blood components, may be made. The calculation of the saturationis performed in a separate subroutine.

Using the ratiometric method three signals S.sub.λa (t), s.sub.λb (t)and S.sub.λc (t) are input to the subroutine. S.sub.λa (t) and S.sub.λb(t) are used to calculate either the primary or secondary referencesignal s'(t) or n'(t). As described above, the wavelengths of light atwhich S.sub.λa (t) and S.sub.λb (t) are measured are chosen to satisfythe relationship of equation (96). Once either the secondary referencesignal n'(t) or the primary reference signal s'(t) is determined, eitherthe primary signal portions s.sub.λa (t) and s.sub.λc (t) or thesecondary signal portions n.sub.λa (t) and n.sub.λc (t) of the measuredsignals S.sub.λa (t) and S.sub.λc (t) are estimated for use incalculation of the oxygen saturation.

The correspondence of the program variables to the variables defined inthe discussion of the joint process estimator is as follows:

Δ_(m) (t)=nc[m].Delta

Γ_(f),m (t)=nc[m].fref

Γ_(b),m (t)=nc[m].bref

f_(m) (t)=nc[m].ferr

b_(m) (t)=nc[m].berr

_(m) (t)=nc[m].Fswsqr

β_(m) (t)=nc[m].Bswsqr

γ_(m) (t)=nc[m].Gamma

ρ_(m),λa (t)=nc[m].Roh₋₋ a

ρ_(m),λc (t)=nc[m].Roh₋₋ c

e_(m),λa (t)=nc[m].err₋₋ a

e_(m),λc (t)=nc[m].err₋₋ c

κ_(m),λa (t)=nc[m].K₋₋ a

κ_(m),λc (t)=nc[m].K₋₋ c

A first portion of the program performs the initialization of theregisters 90, 92, 96, and 98 and intermediate variable values as in the"INITIALIZE CORRELATION CANCELER" box 120 and equations (52) through(56) and equations (73), (74), (77), and (78). A second portion of theprogram performs the time updates of the delay element variables 110where the value at the input of each delay element variable 110 isstored in the delay element variable 110 as in the "TIME UPDATE OF [Z⁻¹] ELEMENTS" box 130.

A third portion of the program calculates the reference signal, as inthe "CALCULATE SECONDARY REFERENCE (n'(t)) oR PRIMARY REFERENCE (s'(t))fOR TWO MEASURED SIGNAL SAMPLES" box 140 using the proportionalityconstant (ω_(av) determined by the ratiometric method as in equation(25).

A fourth portion of the program performs the zero-stage update as in the"ZERO-STAGE UPDATE" box 150 where the zero-stage forward predictionerror f_(o) (t) and the zero-stage backward prediction error b_(o) (t)are set equal to the value of the reference signal n'(t) or s'(t) justcalculated. Additionally, zero-stage values of intermediate variables ₀(t) and β₀ (t) (nc[m].Fswsqr and nc[m].Bswsqr in the program) arecalculated for use in setting register 90, 92, 96, and 98 values in theleast-squares lattice predictor 70 and the regression filters 80a and80b.

A fifth portion of the program is an iterative loop wherein the loopcounter, m, is reset to zero with a maximum of m=NC₋₋ CELLS, as in the"m=0" box 160 in FIG. 9. NC₋₋ CELLS is a predetermined maximum value ofiterations for the loop. A typical value of NC₋₋ CELLS is between 6 and10, for example. The conditions of the loop are set such that the loopiterates a minimum of five times and continues to iterate until a testfor conversion is met or m=NC₋₋ CELLS. The test for conversion iswhether or not the sum of the weighted sum of forward prediction errorsplus the weighted sum of backward prediction errors is less than a smallnumber, typically 0.00001 (i.e, _(m) (t)+β_(m) (t)≦0.00001).

A sixth portion of the program calculates the forward and backwardreflection coefficient Γ_(m),f (t) and Γ_(m),b (t) register 90 and 92values (nc[m].fref and nc[m].bref in the program) as in the "ORDERUPDATE m^(th) -STAGE OF LSL-PREDICTOR" box 170 and equations (61) and(62). Then forward and backward prediction errors f_(m) (t) and b_(m)(t) (nc[m].ferr and nc[m].berr in the program) are calculated as inequations (63) and (64). Additionally, intermediate variables _(m) (t),β_(m) (t) and γ_(m) (t) (nc[m].Fswsqr, nc[m].Bswsqr, nc[m].Gamma in theprogram) are calculated, as in equations (65), (66), and (67). The firstcycle of the loop uses the values for nc[0].Fswsqr and nc[0].Bswsqrcalculated in the ZERO-STAGE UPDATE portion of the program.

A seventh portion of the program, still within the loop calculates theregression coefficient κ_(m),λa (t) and κ_(m),λc (t) register 96 and 98values (nc[m].K₋₋ a and nc[m].K₋₋ c in the program) in both regressionfilters, as in the "ORDER UPDATE m^(th) STAGE OF REGRESSION FILTER(S)"box 180 and equations (68) through (80). Intermediate error signals andvariables e_(m),λa (t), e_(m),λc (t), ρ_(m),λa (t), and ρ_(m),λc (t)(nc[m].err₋₋ a and nc[m].err₋₋ c, nc[m].roh₋₋ a, and nc[m].roh₋₋ c inthe subroutine) are also calculated as in equations (75), (76), (71),and (72), respectively.

The test for convergence of the joint process estimator is performedeach time the loop iterates, analogously to the "DONE" box 190. If thesum of the weighted sums of the forward and backward prediction errors_(m) (t)+β_(m) (t) is less than or equal to 0.00001, the loopterminates. Otherwise, the sixth and seventh portions of the programrepeat.

When either the convergence test is passed or m=NC₋₋ CELLS, an eighthportion of the program calculates the output of the joint processestimator 60 as in the "CALCULATE OUTPUT" box 200. This output is a goodapproximation to both of the primary signals s".sub.λa (t) and s".sub.λc(t) or the secondary signals n".sub.λa (t) and n".sub.λc (t) for the setof samples S.sub.λa (t) and S.sub.λc (t), input to the program. Aftermany sets of samples are processed by the joint process estimator, acompilation of the outputs provides output waves which are goodapproximations to the plethysmographic wave or motion artifact at eachwavelength, λa and λc.

Another copy of a computer program subroutine, written in the Cprogramming language, which calculates either a primary reference s'(t)or a secondary reference n'(t) using the constant saturation method and,using a joint process estimator 60, estimates a good approximation toeither the primary signal portions or secondary signal portions of twomeasured signals, each having a primary portion which is correlated tothe primary reference signal s'(t) and a secondary portion which iscorrelated to the secondary reference signal n'(t) and each having beenused to calculate the reference signals s'(t) and n'(t), is appended inAppendix B. This subroutine is another way to implement the stepsillustrated in the flowchart of FIG. 9 for a monitor particularlyadapted for pulse oximetry. The two signals are measured at twodifferent wavelengths λa and λb, where λa is typically in the visibleregion and λb is typically in the infrared region. For example, in oneembodiment of the present invention, tailored specifically to performpulse oximetry using the constant saturation method, λa=660 nm andλb=940 nm.

The correspondence of the program variables to the variables defined inthe discussion of the joint process estimator is as follows:

Δ_(m) (t)=nc[m].Delta

Γ_(f),m (t)=nc[m].fref

Γ_(b),m (t)=nc[m].bref

f_(m) (t)=nc[m].ferr

b_(m) (t)=nc[m].berr

_(m) (t)=nc[m].Fswsqr

β_(m) (t)=nc[m].Bswsqr

γ(t)=nc[m].Gamma

ρ_(m),λa (t)=nc[m].Roh₋₋ a

ρ_(m),λb (t)=nc[m].Roh₋₋ b

e_(m),λa (t)=nc[m].err₋₋ a

e_(m),λb (t)=nc[m].err₋₋ b

κ_(m),λa (t)=nc[m].K₋₋ a

κ_(m),λb (t)=nc[m].K₋₋ b

First and second portions of the subroutine are the same as the firstand second portions of the above described subroutine tailored for theratiometric method of determining either the primary reference s'(t) orthe noise reference n'(t). The calculation of saturation is performed ina separate module. Various methods for calculation of the oxygensaturation are known to those skilled in the art. One such calculationis described in the articles by G. A. Mook, et al, and Michael R. Neumancited above. Once the concentration of oxygenated hemoglobin anddeoxygenated hemoglobin are determined, the value of the saturation isdetermined similarly to equations (85) through (92) wherein measurementsat times t₁ and t₂ are made at different, yet proximate times over whichthe saturation is relatively constant. For pulse oximetry, the averagesaturation at time t=(t₁ +t₂)/2 is then determined by: ##EQU13##

A third portion of the subroutine calculates either the primaryreference or secondary reference, as in the "CALCULATE PRIMARY ORSECONDARY REFERENCE (s'(t) or n'(t)) FOR TWO MEASURED SIGNAL SAMPLES"box 140 for the signals S.sub.λa (t) and S.sub.λb (t) using theproportionality constants ω_(a) (t) and ω_(v) (t) determined by theconstant saturation method as in equation (3). The saturation iscalculated in a separate subroutine and a value of ω_(a) (t) or ω_(v)(t) is imported to the present subroutine for estimating either theprimary portions s.sub.λa (t) and s.sub.λb (t) or the secondary portionsn.sub.λa (t) and n.sub.λb (t) of the composite measured signals S.sub.λa(t) and S.sub.λb (t).

Fourth, fifth, and sixth portions of the subroutine are similar to thefourth, fifth, and sixth portions of the above described programtailored for the ratiometric method. However, the signals being used toestimate the primary signal portions s.sub.λa (t) and s.sub.λb (t) orthe secondary signal portions n.sub.λa (t) and n.sub.λb (t) in thepresent subroutine tailored for the constant saturation method, areS.sub.λa (t) and S.sub.λb (t), the same signals that were used tocalculate the reference signal s'(t) or n'(t).

A seventh portion of the program, still within the loop begun in thefifth portion of the program, calculates the regression coefficientregister 96 and 98 values κ_(m),λa (t) and κ_(m),λb (t) (nc[m].K₋₋ a andnc[m].K₋₋ b in the program) in both regression filters, as in the "ORDERUPDATE m^(th) STAGE OF REGRESSION FILTER(S)" box 180 and equations (68)through (80). Intermediate error signals and variables e_(m),λa (t)e_(m),λb (t) ρ_(m),λa (t), and ρ_(m),λb (t) (nc[m].err₋₋ a andnc[m].err₋₋ b, nc[m].roh₋₋ a, and nc[m].roh₋₋ b in the subroutine) arealso calculated as in equations (70), (75), (68), and (71),respectively.

The loop iterates until the test for convergence is passed, the testbeing the same as described above for the subroutine tailored for theratiometric method. The output of the present subroutine is a goodapproximation to the primary signals s".sub.λa (t) and s".sub.λb (t) orthe secondary signals n".sub.λa (t) and n".sub.λb (t) for the set ofsamples S.sub.λa (t) and S.sub.λb (t) input to the program. Afterapproximations to the primary signal portions or the secondary signalsportions of many sets of measured signal samples are estimated by thejoint process estimator, a compilation of the outputs provides waveswhich are good approximations to the plethysmographic wave or motionartifact at each wavelength, λa and λb. The estimating process of theiterative loop is the same in either subroutine, only the sample valuesS.sub.λa (t) and S.sub.λc (t) or S.sub.λa (t) and S.sub.λb (t) input tothe subroutine for use in estimation of the primary signal portionss.sub.λa (t) and s.sub.λc (t) or s.sub.λa (t) and s.sub.λb (t) or of thesecondary signal portions n.sub.λa (t) and n.sub.λc (t) or n.sub.λa (t)and n.sub.λb (t) and how the primary and secondary reference signalss'(t) and n'(t) are calculated are different for the ratiometric methodand the constant saturation methods.

Independent of the method used, ratiometric or constant saturation, theapproximations to either the primary signal values or the secondarysignal values are input to a separate subroutine in which the saturationof oxygen in the arterial and venous blood is calculated. If theconstant saturation method is used, the saturation calculationsubroutine also determines values for the proportionality constantsω_(a) (t) and ω_(v) (t) as defined in equation (3) and discussed above.The concentration of oxygenated arterial and venous blood can be foundfrom the approximations to the primary or secondary signal values sincethey are made up of terms comprising x(t), the thickness of arterial andvenous blood in the finger; absorption coefficients of oxygenated andde-oxygenated hemoglobin, at each measured wavelength; and c_(HbO2) (t)and c_(Hb) (t), the concentrations of oxygenated and de-oxygenatedhemoglobin, respectively. The saturation is a ratio of the concentrationof one constituent, A₅, with respect to the total concentration ofconstituents in the volume containing A₅ and A₆ or the ratio of theconcentration of one constituent A₃, with respect to the totalconcentration of constituents in the volume containing A₃ and A₄. Thus,the thickness, x(t), is divided out of the saturation calculation andneed not be predetermined. Additionally, the absorption coefficients areconstant at each wavelength. The saturation of oxygenated arterial andvenous blood is then determined as in equations (107) and (108).

While one embodiment of a physiological monitor incorporating aprocessor of the present invention for determining a reference signalfor use in a correlation canceler, such as an adaptive noise canceler,to remove or derive primary and secondary components from aphysiological measurement has been described in the form of a pulseoximeter, it will be obvious to one skilled in the art that other typesof physiological monitors may also employ the above describedtechniques.

Furthermore, the signal processing techniques described in the presentinvention may be used to compute the arterial and venous blood oxygensaturations of a physiological system on a continuous or nearlycontinuous time basis. These calculations may be performed, regardlessof whether or not the physiological system undergoes voluntary motion.The arterial pulsation induced primary plethysmographic signals s.sub.λa(t) and s.sub.λb (t) may be used to compute arterial blood oxygensaturation. The primary signals s.sub.λa (t) and s.sub.λb (t) can alwaysbe introduced into the measured signals S.sub.λ a(t) and S.sub.λ b(t) ifat least two requirements are met. The two requirements include theselection of two or more flesh penetrating and blood absorbingwavelengths which are optically modulated by the arterial pulsation andan instrument design which passes all or portions of all electromagneticsignals which are related to the pulsation. Similarly, the secondarysignals n.sub.λa (t) and n.sub.λb (t) related to venous blood flow maybe used to compute its corresponding oxygen saturation. The secondarysignal components n.sub.λ a(t) and n.sub.λb (t) can be guaranteed to becontained in the measured signals S.sub.λa (t) and S.sub.λ b(t) if thetwo or more flesh penetrating and blood absorbing wavelengths areprocessed to pass all or portions of all electromagnetic signalsrelating to venous blood flow. This may include but is not limited toall or portions of all signals which are related to the involuntaryaction of breathing. Similarly, it must be understood that there aremany different types of physical systems which may be configured toyield two or more measurement signals each possessing a primary andsecondary signal portion. In a great many of such physical systems itwill be possible to derive one or more reference signals. The referencesignals may be used in conjunction with a correlation canceler, such asan adaptive noise canceler, to derive either the primary and/orsecondary signal components of the two or more measurement signals on acontinuous or intermittent time basis.

Another embodiment of a physiological monitor incorporating a processorof the present invention for determining a reference signal for use in acorrelation canceler, such as an adaptive noise canceler, to remove orderive primary and secondary components from a physiological measurementmay be described in the form of a instrument which measures bloodpressure. There are several ways of obtaining blood pressuremeasurements, such as tonometry, and pulse wave velocity. Both of thesemethods are substantially related to plethysmography.

Tonometry is a measurement method in which a direct reading of thearterial pressure pulse is made non-invasively. These measurements areinvariably made through the use of a piezoelectric force transducer, thesurface of which is gently pressed against a near-surface arterysupported by underlying bone. If the transducer is sufficiently pressedagainst the artery that its surface is in complete contact with thetissue; then, knowing its surface area, its output can be directly readas pressure. This "flattening" of the arterial wall leads to the name ofthis method, applanation tonometry. The pulse wave velocity techniquerelies on the concept that the speed with which the pressure pulse,generated at the heart, travels "down" the arterial system is dependenton pressure. In each of these cases plethysmographic waveforms are usedto determine the blood pressure of a patient.

Furthermore, it will be understood that transformations of measuredsignals other than logarithmic conversion and determination of aproportionality factor which allows removal or derivation of the primaryor secondary signal portions for determination of a reference signal arepossible. Additionally, although the proportionality factor ω has beendescribed herein as a ratio of a portion of a first signal to a portionof a second signal, a similar proportionality constant determined as aratio of a portion of a second signal to a portion of a first signalcould equally well be utilized in the processor of the presentinvention. In the latter case, a secondary reference signal wouldgenerally resemble n'(t)=n.sub.λb (t)-ωn.sub.λa (t).

Furthermore, it will be understood that correlation cancellationtechniques other than joint process estimation may be used together withthe reference signals of the present invention. These may include butare not limited to least mean square algorithms, wavelet transforms,spectral estimation techniques, neural networks, Weiner filters, Kalmanfilters, QR-decomposition based algorithms among others. Theimplementation that we feel is the best, as of this filing, is thenormalized least square lattice algorithm an implementation of which islisted in Appendix C.

It will also be obvious to one skilled in the art that for mostphysiological measurements, two wavelengths may be determined which willenable a signal to be measured which is indicative of a quantity of acomponent about which information is desired. Information about aconstituent of any energy absorbing physiological material may bedetermined by a physiological monitor incorporating a signal processorof the present invention and an correlation canceler by determiningwavelengths which are absorbed primarily by the constituent of interest.For most physiological measurements, this is a simple determination.

Moreover, one skilled in the art will realize that any portion of apatient or a material derived from a patient may be used to takemeasurements for a physiological monitor incorporating a processor ofthe present invention and a correlation canceler. Such areas include adigit such as a finger, but are not limited to a finger.

One skilled in the art will realize that many different types ofphysiological monitors may employ a signal processor of the presentinvention in conjunction with a correlation canceler, such as anadaptive noise canceler. Other types of physiological monitors include,but are in not limited to, electron cardiographs, blood pressuremonitors, blood gas saturation (other than oxygen saturation) monitors,capnographs, heart rate monitors, respiration monitors, or depth ofanesthesia monitors. Additionally, monitors which measure the pressureand quantity of a substance within the body such as a breathalizer, adrug monitor, a cholesterol monitor, a glucose monitor, a carbon dioxidemonitor, a glucose monitor, or a carbon monoxide monitor may also employthe above described techniques for removal of primary or secondarysignal portions.

Furthermore, one skilled in the art will realize that the abovedescribed techniques of primary or secondary signal removal orderivation from a composite signal including both primary and secondarycomponents can also be performed on electrocardiography (ECG) signalswhich are derived from positions on the body which are close and highlycorrelated to each other. It must be understood that a tripolarLaplacian electrode sensor such as that depicted in FIG. 32 which is amodification of a bipolar Laplacian electrode sensor discussed in thearticle "Body Surface Laplacian ECG Mapping" by Bin He and Richard J.Cohen contained in the journal IEEE Transactions on BiomedicalEngineering, Vol. 39, No. 11, November 1992 could be used as an ECGsensor. This article is hereby incorporated as reference. It must alsobe understood that there are a myraid of possible ECG sensor geometry'sthat may be used to satisfy the requirements of the present invention.

Furthermore, one skilled in the art will realize that the abovedescribed techniques of primary or secondary signal removal orderivation from a composite signal including both primary and secondarycomponents can also be performed on signals made up of reflected energy,rather than transmitted energy. One skilled in the art will also realizethat a primary or secondary portion of a measured signal of any type ofenergy, including but not limited to sound energy, X-ray energy, gammaray energy, or light energy can be estimated by the techniques describedabove. Thus, one skilled in the art will realize that the processor ofthe present invention and a correlation canceler can be applied in suchmonitors as those using ultrasound where a signal is transmitted througha portion of the body and reflected back from within the body backthrough this portion of the body. Additionally, monitors such as echocardiographs may also utilize the techniques of the present inventionsince they too rely on transmission and reflection.

While the present invention has been described in terms of aphysiological monitor, one skilled in the art will realize that thesignal processing techniques of the present invention can be applied inmany areas, including but not limited to the processing of aphysiological signal. The present invention may be applied in anysituation where a signal processor comprising a detector receives afirst signal which includes a first primary signal portion and a firstsecondary signal portion and a second signal which includes a secondprimary signal portion and a second secondary signal portion. The firstand second signals propagate through a common medium and the first andsecond primary signal portions are correlated with one another.Additionally, at least a portion of the first and second secondarysignal portions are correlated with one another due to a perturbation ofthe medium while the first and second signals are propagating throughthe medium. The processor receives the first and second signals and maycombine the first and second signals to generate a secondary referencein which is uncorrelated with the primary signal portions of themeasured signals or a primary reference which is uncorrelated with thesecondary signal portions of the measured signals. Thus, the signalprocessor of the present invention is readily applicable to numeroussignal processing areas.

We claim:
 1. In combination:a detector responsive to a first signalwhich travels along a first propagation path and a second signal whichtravels along a second propagation path, to provide a representation ofsaid first and said second signals on an output, a portion of said firstand second propagation paths being located in the same propagationmedium, wherein said representation of said first signal on said outputhas a primary signal portion and a secondary signal portion, saidprimary signal portion of said first signal being subject to attenuationalong substantially the entire first propagation path and wherein saidrepresentation of said second signal on said output has a primary signalportion and a secondary signal portion, said primary signal portion ofsaid second signal being subject to attenuation along substantially theentire second propagation path; and a first signal processor havinginputs coupled to said detector, said first signal processor responsiveto said representation of said first and second signals from saiddetector to combine said first and second signals to generate either aprimary or secondary reference signal which is a function significantlyof either of, respectively, said primary or said secondary signalportions of said first and second signals.
 2. The combination recited inclaim 1, further comprising a second signal processor responsive to thesecondary reference signal and to said representation of said firstsignal to derive therefrom an output signal which is a function ofsignificantly said primary signal portion of said first signal.
 3. Thecombination recited in claim 2, wherein said second signal processorcomprises a correlation canceler.
 4. The combination recited in claim 2,wherein said second signal processor comprises an adaptive noisecanceler.
 5. The combination recited in claim 4, wherein said adaptivenoise canceler comprises a joint process estimator.
 6. The combinationrecited in claim 5, wherein said joint process estimator comprises aleast-squares lattice predictor and a regression filter.
 7. Thecombination recited in claim 1, further comprising a second signalprocessor responsive to said primary reference signal and to saidrepresentation of said first signal to derive therefrom an output signalwhich is a function significantly of said secondary signal portion ofsaid first signal.
 8. The combination recited in claim 1, furthercomprising a second signal processor responsive to said secondaryreference signal and to said representation of said first signal toderive therefrom an output signal which is a function significantly ofsaid primary signal portion of said second signal.
 9. The combinationrecited in claim 1, further comprising a second signal processorresponsive to said secondary reference signal and to said representationof said first signal to derive therefrom an output signal having asignificant component which is a function of said secondary signalportion of said second signal.
 10. The combination recited in claim 1,wherein said detector is configured to detect a physiological functionrepresented by said first and second signals.
 11. The combinationrecited in claim 10, wherein said detector is adapted to measure a bloodconstituent.
 12. The combination recited in claim 11, wherein the bloodconstituent measured by said detector is blood gas.
 13. The combinationrecited in claim 10, wherein said detector comprises a sensor that isresponsive to electromagnetic energy.
 14. The combination recited inclaim 1, further comprising electromagnetic means connected to saiddetector for measuring a plethysmographic waveform depending upon saidfirst and second signals received by said detector through saidpropagation medium, said propagation medium including living tissue. 15.The combination recited in claim 1, further comprising a pulse oximeterconnected to said detector, said pulse oximeter monitoring aphysiological condition depending upon said first and second signalsreceived by said detector through said propagation medium, saidpropagation medium including living tissue.
 16. The combination recitedin claim 1, further comprising a blood pressure monitor connected tosaid detector and configured to derive a physiological conditiondepending upon said first and second signals received by said detectorthrough said propagation medium, said propagation medium includingliving tissue.
 17. The combination recited in claim 1, furthercomprising an electrocardiograph connected to said detector saidelectrocardiograph adapted to determine an electrocardiogram conditiondepending upon said first and second signals received by said detectormeans through said propagation medium, said propagation medium includingliving tissue.
 18. The combination recited in claim 17, wherein saidelectrocardiograph includes a tripolar electrode sensor having threeconcentrically arranged electrodes.
 19. An apparatus for computingarterial and venous signals in living tissue, said apparatuscomprising:a detector configured to receive a first signal which travelsalong a first propagation path and a second signal which travels along asecond propagation path, at least a portion of said first and secondpropagation paths being located in a propagation medium, wherein saidfirst signal has an arterial signal portion that is indicative ofarterial blood and another signal portion that is indicative of venousblood, and said second signal has an arterial signal portion that isindicative of arterial blood and another signal portion that isindicative of venous blood; and signal processor means having an inputcoupled to said detector and responsive to said first and second signalsand to combine said first and second signals to generate a signal havinga significant component which is a function of either of said arterialor said other signal portions of said first and second signal.
 20. Theapparatus recited in claim 19, wherein the other signal portion of eachof said first and second signals includes an indication of humanrespiration.
 21. A signal processor comprising:a detector responsive toa first signal which travels along a first propagation path and a secondsignal which travels along a second propagation path, to provide arepresentation of said first and said second signals on an output, atleast a portion of said first and second propagation paths being locatedin the same propagation medium, wherein said representation of saidfirst signal on said output has a primary signal portion and a secondarysignal portion, and wherein said representation of said second signal onsaid output has a primary signal portion and a secondary signal portion;a first signal processor having inputs coupled to said detector, saidfirst signal processor responsive to said representations of said firstand second signals from said detector to combine said first and secondsignals to generate either a primary or secondary reference signal whichis a function significantly of either of, respectively, said primary orsaid secondary signal portions of said first and second signals; and asecond signal processor responsive to the secondary reference signal andto said representation of said first signal to derive therefrom anoutput signal which is a function of significantly said primary signalportion of said first signal.
 22. A signal processor comprising:adetector responsive to a first signal which travels along a firstpropagation path and a second signal which travels along a secondpropagation path, to provide a representation of said first and saidsecond signals on an output, at least a portion of said first and secondpropagation paths being located in the same propagation medium, whereinsaid representation of said first signal on said output has a primarysignal portion and a secondary signal portion, and wherein saidrepresentation of said second signal on said output has a primary signalportion and a secondary signal portion; a first signal processor havinginputs coupled to said detector, said first signal processor responsiveto said representations of said first and second signals from saiddetector to combine said first and second signals to generate either aprimary or secondary reference signal which is a function significantlyof either of, respectively, said primary or said secondary signalportions of said first and second signals; and a second signal processorresponsive to said primary reference signal and to said representationof said first signal to derive therefrom an output signal which is afunction significantly of said secondary signal portion of said firstsignal.
 23. A signal processor comprising:a detector responsive to afirst signal which travels along a first propagation path and a secondsignal which travels along a second propagation path, to provide arepresentation of said first and said second signals on an output, atleast a portion of said first and second propagation paths being locatedin the same propagation medium, wherein said representation of saidfirst signal on said output has a primary signal portion and a secondarysignal portion, and wherein said representation of said second signal onsaid output has a primary signal portion and a secondary signal portion;a first signal processor having inputs coupled to said detector, saidfirst signal processor responsive to said representations of said firstand second signals from said detector to combine said first and secondsignals to generate either a primary or secondary reference signal whichis a function significantly of either of, respectively, said primary orsaid secondary signal portions of said first and second signals; and asecond signal processor responsive to said secondary reference signaland to said representation of said first signal to derive therefrom anoutput signal which is a function significantly of said primary signalportion of said second signal.
 24. A signal processor comprising:adetector responsive to a first signal which travels along a firstpropagation path and a second signal which travels along a secondpropagation path, to provide a representation of said first and saidsecond signals on an output, at least a portion of said first and secondpropagation paths being located in the same propagation medium, whereinsaid representation of said first signal on said output has a primarysignal portion and a secondary signal portion, and wherein saidrepresentation of said second signal on said output has a primary signalportion and a secondary signal portion; a first signal processor havinginputs coupled to said detector, said first signal processor responsiveto said representations of said first and second signals from saiddetector to combine said first and second signals to generate either aprimary or secondary reference signal which is a function significantlyof either of, respectively, said primary or said secondary signalportions of said first and second signals; and a second signal processorresponsive to said secondary reference signal and to said representationof said first signal to derive therefrom an output signal having asignificant component which is a function of said secondary signalportion of said second signal.